Research ArticlePreliminary Evaluation of Gaofen-3 Quad-Polarized SARImagery for Longbao Protected Plateau Wetland Reserve

QiufangWei123 Yun Shao 123 and XiaochenWang 123

1 Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing 100101 China2Laboratory of Target Microwave Properties Deqing Academy of Satellite Applications Deqing Zhejiang Province 313200 China3University of Chinese Academy of Sciences Beijing 100049 China

Correspondence should be addressed to XiaochenWang wangxcradiaccn

Received 23 December 2018 Revised 17 June 2019 Accepted 24 July 2019 Published 20 August 2019

Academic Editor Mohammad Haider

Copyright copy 2019 Qiufang Wei et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) satellite launched by the China Academy of Space Technology isunder continuous operating mode at C-band since September 2016 The main objective of this study was to present a preliminaryevaluation of GF-3 quad-polarized SAR imagery for protected wetland classification Four scenes of GF-3 quad-polarized SARimageries were collected in this study and analyzed systematically Besides the GF-2 optical imagery and in situ survey resultswere also collocated as reference data It was verified that the polarized features including double bounce scattering component119875119889119887119897 odd bounce scattering component 119875119900119889119889 and volume scattering component 119875V119900119897 from Freeman decomposition algorithm werewell applied in distinguishing typical coverage of Longbao protected plateau wetland In consideration of robustness of classifierthe method of Maximum Likelihood Classification was investigated for classification based on selected polarized features TheKappa coefficient and overall accuracy of classification mapping are 075 and 8216 respectively which revealed that the GF-3quad-polarized SAR can provide a satisfying mapping for classification of Longbao protected plateau wetland

1 Introduction

Wetland a multifunction ecosystem on the earth plays a keyrole in the human survival and environmental sustainabledevelopment Wetland has gradually become a headlinegoal of ecosystem monitoring not only for evaluation ofresource development but also for maintenance of regionalecological environment [1ndash3] For precise monitoring ofwetland remote sensing technology has beenwidely acceptedby researchers with advantages of it being large-scale andcost effective [4 5] In the past decades significant capabil-ity of space-borne Synthetic Aperture Radar (SAR) sensortoward wetland monitoring and observation in high spatialresolution under all-weather conditions has been revealedCompared to single- or dual-polarized SAR such as Canadasatellite Radarsat-2 and Japan satellite Alos-2 quad-polarizedSAR is occupied by more polarization status which canprovide more scattering details to meet the demand of finewetlandmonitoring [6ndash9] Some studies revealed the capabil-ity of orbited quad-polarized SAR for wetland classification

Gou et al [10] proposed an unsupervised method based on athree-channel joint sparse representation classification withfully polarimetric SAR (PolSAR) data The proposed methodutilized both texture and polarimetric feature informationextracted from theHHHV andVVchannels of a SAR imageBuono et al [11] demonstrated the PolSAR capabilities toclassify coastal areas of The Yellow River delta (China) byapplying two well-known unsupervised classifiers namelythe H120572-based and the FreemanndashDurden (FD) model-basedalgorithms to a fully polarimetric SAR scene collected byusing RADARSAT-2 in 2008 Recently Chen et al [12]proposed a novel quad-polarized SAR image classificationmethod based on multilayer autoencoders and self-pacedlearning Wang et al [13] proposed a new classificationscheme for mud and sand flats on intertidal flats by using FDand CloudendashPottier polarimetric decomposition

Noteworthy the C band multipolarization SAR Gaofen-3(GF-3) satellite was launched into a polar sun-synchronousorbit of 755 km altitude with a 26-day repeat cycle on 10August 2016 [14 15] As the first Chinese civil operating

HindawiJournal of SensorsVolume 2019 Article ID 8789473 7 pageshttpsdoiorg10115520198789473

2 Journal of Sensors

Table 1 List of quad-polarized GF-3 SAR specifications

Satellite Scene ID Acquisition Time (UTC) Pol Resolutionm SwathskmGF-3 3377378 2017-2-27 QPSI 8 30GF-3 3741512 2017-6-6 QPSI 8 30GF-3 3842536 2017-7-5 QPSI 8 30GF-3 5714664 2018-11-3 QPSI 8 30

SAR platform it is significant to make a preliminary assess-ment for further application Some preliminary analysiswas also undertaken recently An et al [16] used two GF-3 SAR imageries to extract the coastlines of the regionsof Bohai and Taihu in China respectively Guo et al [17]investigated the capability of quad-polarized SAR for landcover type classification using deep highway unit networksmodel

Although GF-3 SAR has been operational for a period oftime only a few studies revealed its polarization capabilityThe application performance of GF-3 polarimetric SAR datastill needs to be fully assessed Besides considering thesignificant status of GF-3 SAR in Chinese development ofspaceborne SAR it is urgent to perform capability evaluationof GF-3 SAR for different observation scenes The mainobjective of this study was to demonstrate the potentialof GF-3 quad-polarized SAR and present a preliminaryquantitative result for wetland classification To make aninterpretation of GF-3 SAR for wetland monitoring besidesthe normalized radar cross-section (NRCS) processing theknown polarization decomposition algorithms of Freemanand HAAlpha decomposition were further applied to fourscenes of GF-3 quad-polarized SAR imageries collected overLongbao protected plateau wetland

2 Study Area and Dataset

21 Study Area Longbao protected plateau wetland (3311ndash3327∘N 9639ndash9669∘E) located in Longbao town of Yushucountry Qinghai province is a typical protected plateauwetland that is approximately 14 km long with a maximumwidth of about 35 km (Figure 1) The maximum depth ofthe wetland has been reported to be about 4 m howevermost part of the wetland is generally much shallowThe entirenature reserve is flanked by steep ridges that typically riseup to 750 m above the valley floor The wetland is fed bygroundwater streams precipitation snowmelt and it drainsinto the Yi Chu River to the northwest a short tributary ofthe nearby Tongtian River

22 Remote Sensing Data

221 GF-3 Quad-Polarized SAR Data Four scenes of GF-3quad-polarized SAR imageries were collected in this studyThemain specifications of GF-3 quad-polarized SAR used arelisted in Table 1 The imagery coverage of collected GF-3 SARis shown in Figure 1 together with Longbao protected plateauwetland scope

Figure 1 Imageries coverage of collected remote sensing dataBlue squares represent the GF-3 quad-polarized SAR Red squarerepresents the GF-2 optical imagery Yellow square represents theLongbao protected plateau wetland scope

222 GF-2 Optical Data A scene of GF-2 optical imagerywas collected in this study The main specifications of GF-2 optical imagery used herein are listed in Table 2 Theimagery coverage of collected GF-2 optical imagery is shownin Figure 1 together with Longbao protected plateau wetlandscope

223 In Situ Survey Data An in situ survey for Longbaoprotected plateau wetland was undertaken to acquire mainwetland types and ground distribution characteristics Thein situ survey route is presented in Figure 2 Four typesof Longbao protected plateau wetland coverage includingalpine tundra alpine meadow swamp meadow and lakeswere surveyed and pictured during the survey as shown inFigure 3

3 Experiment and Results

31 Selection of Polarization Features Prior to efficient detec-tion of change in Longbao protected plateau wetland fromSAR imagery the polarization features need to be derivedfrom the raw polarization scattering matrix [119878] accordingto Table 3 Twelve polarization features were proposed forstatistical and comparative analysis including three backscat-tering coefficients of 120590ℎℎ 120590ℎV and 120590VV scattering entropy119867 scattering angle 120572 Anisotropy 119860 three eigenvalues of1198971 1198972 and 1198973 double bounce scattering component 119875119889119887119897 odd

Journal of Sensors 3

Table 2 List of GF-2 optical specifications

Satellite Scene ID Acquisition Time (UTC) Mode Resolutionm SwathskmGF-2 5819684 2018-12-01 PMS 4 45

Table 3 The polarized features used in this study

Polarized features DefinitionThe Backscattering coefficient 1205900ℎℎ = ⟨119878ℎℎ119878

lowastℎℎ⟩ 120590

0VV = ⟨119878VV119878

lowastVV⟩ 1205900ℎV = ⟨119878ℎV119878

lowastℎV⟩

Entropy 119867 = sum3119894=1 minus119901119894log3119901119894 (119901119894 = 120582119894sum3119895=1 120582119895)

Scattering angle 120572 = p11205721 + p21205722 + p31205723Anisotropy A = (1205822 minus 1205823) (1205822 + 1205823)Double bounce scattering component 119875119889119887119897 = 119891119889119887119897(1 + |120572|

2)

Odd bounce scattering component 119875119900119889119889 = 119891119900119889119889(1 +100381610038161003816100381612057310038161003816100381610038162)

Volume scattering component 119875V119900119897 = (83) 119891V119900119897

bounce scattering component 119875119900119889119889 and volume scatteringcomponent 119875V119900119897

Notably the 119878ℎℎ 119878ℎV and 119878VV are the elements of Sinclairmatrix 120582i is the i-th eigenvalue of coherence matrix [119879]120572i is derived from the i-th eigenvalue of coherence matrix[119879] 119891119889119887119897 119891119900119889119889 and 119891V119900119897 are double bounce scattering surfacescattering and volume scattering coefficients respectively120572 and 120573 are the processed variables used to describe therelative advantages between different scattering mechanismslt gt represents ensemble average lowast represents conjugateoperation and Re denotes real part

In order to precisely select the optimal polarizationfeature or subset radiometric calibration geometric re-projection and filtering de-noising operations were per-formed on raw SAR data Besides two polarization decom-position algorithms including Freeman decomposition andHAAlpha decomposition were applied for collection ofpolarization features which are listed in Table 2 Noteworthyall polarization features were operated under same size ofde-noising window (3 times 3) and same coordinate system toguarantee registration in unified scaleTheRGB pseudo colorimageries are shown in Figure 4

Quantitative analysis of each polarization feature wasalso necessary for selection of optimal polarization featureor subset With the objective of discriminating four typesof wetland coverage each feature listed in Table 2 wasinvestigated into change curves shown in Figure 5 Figure 5(a)exhibits that the NRCS variations of HH HV and VV areslightly obvious but not enough for precisely differentiatealpine tundra alpine meadow swamp meadow and lakesFor the abovementioned four types of coverages the biggestbiases of NRCS are 264 176 and 214 dB respectively forHH HV and VV polarization Moreover the Freeman andHAAlpha decomposition components were also proposedin this section Noteworthy the decomposition componentswere normalized by using (1) to keep all parameters in samescale

119865119894 =119865isum3i=1 119865119894

(1)

Notably F119894 is the polarized features derived fromFreemanand HAAlpha decomposition

Figure 5(b) demonstrates that the performance of 120572 canwell differentiate lake swamp meadow and alpine meadowbut not alpine tundra and alpinemeadowTheperformance ofboth H and 119860 shows similar curves for lake swamp meadowalpine meadow and alpine tundra and the characteristicof curves are not as clear as 120572 Figure 5(c) represents theFreeman decomposition component A sharp curve of 119875119889119887119897represents the dominance of double scattering mechanism inclassifying the coverages of the Longbao protected plateauwetland Although small bias exists in the performance of119875119900119889119889 and 119875V119900119897 it is still satisfying enough to differentiate lakeswamp meadow alpine meadow and alpine meadow witheither polarized feature Considering the impact of polarizedfeatures in Longbao protected plateau wetland mapping itwas suggested to adopt the polarized features 119875119889119887119897 119875V119900119897 and119875V119900119897 from Freeman decomposition as main factors for furtherclassification input

32 Maximum Likelihood Classification Herein the selectedpolarization parameters were measured for Longbao pro-tected plateau wetland coverage type classification In consid-eration of robustness of classifier the method of MaximumLikelihood Classification (MLC) is widely applied in polar-ized SAR classification [18 19] In this study the MLC wasselected for classification evaluation

The steps for selected polarization features for Longbaoplateau wetland classification performance are listed as fol-lows(1) 80000 pixels are randomly selected as training input

of classifier among which four typical Longbao protectedplateau wetland coverage includes alpine tundra alpinemeadow swamp meadow and lakes(2)The selected pixels are input into classifier with each

tag of coverage type The iteration process stops until thespecified pixel is classified into a certain types of alpinetundra alpine meadow swamp meadow and lakes(3) Another 80000 pixels are used for verification of

classification performanceOwing to absence of in situ surveydata the high resolution GF-2 optical imageries are used

4 Journal of Sensors

Figure 2The in situ survey route for Longbao protected plateau wetland

alpine tundra alpine meadow swamp meadow lakes

Figure 3 Types of Longbao protected plateau wetland coverage

(a) (b) (c)

Figure 4 Pseudo colour imageries of raw polarization channel Freeman decomposition and HAAlpha decomposition results (a) R 120590ℎℎG 120590VV B 120590ℎV (b) R 119867 G119860 B 120572 (c) R119875119889119887119897 G119875119900119889119889 B119875V119900119897

as reference data Moreover the assessment index of Kappacoefficient and average classification accuracy can be derivedfrom confusion matrix(4) Operations are repeated for all three scenes of GF-

3 SAR imageries Based on the proposed Kappa coefficientand average classification accuracy the evaluation of GF-3quad-polarized SAR imagery for Longbao protected plateauwetland classification can be provided

Following the abovementioned steps the classificationmapping is shown in Figure 6 Brown area represents thelakes Yellow area represents the swamp meadow whichdistributes over the lakesides Green area represents thealpine meadow and blue area represents the alpine tundraFor further evaluation of the classification performance theconfusion matrix was derived and Kappa coefficient andmapping accuracy are presented in Table 4 Clearly the lake(accuracy = 975) is easily distinguished from other threecoverage types compared to the alpine tundra (accuracy =

9166)The accuracy of swampmeadow and alpinemeadowis 5350 and 6724 respectively which can be attributedto the similar geometric structure of these two coveragetypes All in all the overall accuracy is 821592 and kappacoefficient is 07517

4 Discussion

Although the classification result of GF-3 quad-polarizedSAR imagery for Longbao protected plateauwetland providessatisfying evaluation a lot more systematic explorations aredemanded in future study

(1) Calibration Precision According to the calibration formulaof equation (5) the NRCS of HH HV VV is -2682 -3803and -2763 dB respectively However the noise equivalentsigma zero (NESZ) of RADARSAT-2 standard quad-pol datais -32 dB Considering the design parameters of GF-3 SAR

Journal of Sensors 5

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

NRC

S d

B

minus24minus26minus28minus30minus32minus34minus36minus38minus40

HHHVVV

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

Ratio

of n

orm

aliz

ed p

aram

eter 034

032

03028026

024022

02

EntropyAnisotropyAlpha

08070605040302

001

Ratio

of n

orm

aliz

ed p

aram

eter

DblOddVol

Figure 5 Polarized features response curves for different types of Longbao protected plateau wetland coverage

Table 4 Classification performance of Longbao protected plateau wetland in 2018-07-05

Ground Truth ()Class Lake Swamp meadow Alpine meadow Alpine tundra TotalLake 9750 000 000 003 3893Swamp meadow 000 5350 306 028 1075Alpine meadow 000 2374 6724 803 1900Alpine tundra 250 2276 2970 9166 3132Total 10000 10000 10000 10000 10000Overall Accuracy = 821592Kappa Coefficient = 07517

6 Journal of Sensors

Alpine tundraAlpine meadowSwamp meadowLakes

33∘14N

33∘11N

33∘10N

33∘14N

33∘11N

33∘10N

96∘30

E 96∘32

E 96∘34

E

96∘30

E 96∘32

E 96∘34

E

Figure 6 Classification mapping of Longbao protected plateau wetland in 2018-07-05

there is a need to discuss and re-adjust the calibrationaccuracy of each polarization channel in particular for HVchannel

(2) Absence of Geometric Parameter of Coverages TypesOwing to the absence of geometric parameter of coveragestypes in particular the plants height above ground orwater body lake or swamp water level and so on onlyquantitative classification accuracy of lake swamp meadowalpine meadow and alpine tundra are given In future study adetailed in situ survey will be carried out for synchronizationacquisition of satellite-earth information The difference ofpolarized features for different coverage types will be relatedto collected ground information More polarized scatteringcharacteristics will be revealed in order to improve the clas-sification performance of Longbao protected plateau wetlandwith GF-3 quad-polarized SAR

5 Conclusion

This study proposed a preliminary evaluation of GF-3 quad-polarized SAR imagery for Longbao protected plateau wet-land classification The main conclusions are as follows Theimaging capability of GF-3 quad-polarized SAR for Long-bao protected plateau wetland was assessed from collecteddataset Beside NRCS the Freeman and HAAlpha decom-position components were also investigated Among all listedpolarized features the double bounce scattering component119875119889119887119897 double bounce scattering component 119875119900119889119889 and volumescattering component 119875V119900119897 from Freeman decompositionalgorithm dominated in distinguishing typical coverage ofLongbao protected plateau wetland Based on MLC theKappa coefficient and average accuracy of Longbao protectedplateau wetland classification mapping were 075 and 8216respectively It was thus verified that the GF-3 quad-polarized

Journal of Sensors 7

SAR can provide a satisfying mapping for wetland classifica-tion

Data Availability

The remote sensing data used to support the findingsof this study have been deposited in the China Centerfor Resources Satellite Data and Application repository(httpwwwcresdacomCN)

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors gratefully acknowledge the financial supportfrom the National Key Research and Development Programof China (2016YFB0502504 2016YFB0502500) and NationalScience Foundation of China (Grant nos 41431174 61471358and 41401427)This article is partly sponsored by the Fundingof Scholarship of Chinese Academy of Sciences (Grant noY8SY1400CX)

References

[1] C Liquete G Zulian I Delgado A Stips and J MaesldquoAssessment of coastal protection as an ecosystem service inEuroperdquo Ecological Indicators vol 30 pp 205ndash217 2013

[2] J Brenner J A Jimenez R Sarda and A Garola ldquoAnassessment of the non-market value of the ecosystem servicesprovided by the Catalan coastal zone Spainrdquo Ocean amp CoastalManagement vol 53 no 1 pp 27ndash38 2010

[3] K Pike P Wright B Wink and S Fletcher ldquoThe assessment ofcultural ecosystem services in the marine environment using Qmethodologyrdquo Journal of Coastal Conservation vol 19 no 5 pp667ndash675 2015

[4] A Niedermeier E Romaneeıen and S Lehner ldquoDetectionof coastlines in SAR images using wavelet methodsrdquo IEEETransactions on Geoscience and Remote Sensing vol 38 no 5pp 2270ndash2281 2000

[5] B R Rao R S Dwivedi S P Kushwaha S N BhattacharyaJ B Anand and S Dasgupta ldquoMonitoring the spatial extent ofcoastal wetlands using ERS-1 SAR datardquo International Journal ofRemote Sensing vol 20 no 13 pp 2509ndash2517 2010

[6] X Ding F Nunziata X Li and M Migliaccio ldquoPerformanceanalysis and validation of waterline extraction approachesusing single- and dual-polarimetric SAR datardquo IEEE Journalof Selected Topics in Applied Earth Observations and RemoteSensing vol 8 no 3 pp 1019ndash1027 2015

[7] A Al Fugura L Billa and B Pradhan ldquoSemi-automatedprocedures for shoreline extraction using single RADARSAT-1 SAR imagerdquo Estuarine Coastal and Shelf Science vol 95 no4 pp 395ndash400 2011

[8] L Ge X Li F Wu and I L Turner ldquoCoastal erosion mappingthrough intergration of SAR and Landsat TM imageryrdquo inProceedings of the 2013 33rd IEEE International Geoscienceand Remote Sensing Symposium IGARSS 2013 pp 2266ndash2269Australia July 2013

[9] M F Bruno M G Molfetta M Mossa R Nutricato AMorea and M T Chiaradia ldquoCoastal observation throughcosmo-skymed high-resolution SAR imagesrdquo Journal of CoastalResearch vol 75 no sp1 pp 795ndash799 2016

[10] S Gou X Li andX Yang ldquoCoastal zone classificationwith fullypolarimetric SAR imageryrdquo IEEE Geoscience amp Remote SensingLetters vol 13 pp 1ndash5 2016

[11] A Buono F Nunziata MMigliaccio X Yang and X Li ldquoClas-sification of the Yellow River delta area using fully polarimetricSAR measurementsrdquo International Journal of Remote Sensingvol 38 no 23 pp 6714ndash6734 2017

[12] W Chen S Gou X Wang X Li and L Jiao ldquoClassification ofPolSAR images using multilayer autoencoders and a self-pacedlearning approachrdquo Remote Sensing vol 10 no 1 2018

[13] WWang X Yang X Li et al ldquoA fully polarimetric SAR imageryclassification scheme for mud and sand flats in intertidal zonesrdquoIEEE Transactions on Geoscience and Remote Sensing vol 55no 3 pp 1734ndash1742 2017

[14] Q Zhang and Y Liu ldquoOverview of chinese first C band multi-polarization SAR satellite GF-3rdquoAerospace China vol 18 no 32017

[15] Y Chang P Li J Yang et al ldquoPolarimetric calibration andquality assessment of the gf-3 satellite images[J]rdquo Sensors vol18 no 2 p 403 2018

[16] M An Q Sun J Hu Y Tang and Z Zhu ldquoCoastline detectionwith gaofen-3 SAR images using an improved FCM methodrdquoSensors vol 18 no 6 article no 1898 p 431 2018

[17] Y Guo E Chen Y Guo Z Li C Li and K Xu ldquoDeep highwayunit network for land cover type classification with GF-3 SARimageryrdquo in Proceedings of the 2017 SAR in Big Data EraModelsMethods and Applications BIGSARDATA 2017 pp 1ndash6 ChinaNovember 2017

[18] M SMajd E Simonetto andL Polidori ldquoMaximum likelihoodclassification of single highresolution polarimetric SAR Imagesin urban areasrdquo Photogrammetrie Fernerkundung Geoinforma-tion vol 2012 no 4 pp 395ndash407 2012

[19] J R Otukei T Blaschke and M Collins ldquoUsing the Wishartmaximum likelihood classifier for assessing the potential ofTerraSAR-X and ALOS PALSAR data for land cover mappingrdquoInternational Journal of Image and Data Fusion vol 5 no 2 pp138ndash151 2014

Journal of Sensors 3

Table 2 List of GF-2 optical specifications

Satellite Scene ID Acquisition Time (UTC) Mode Resolutionm SwathskmGF-2 5819684 2018-12-01 PMS 4 45

Table 3 The polarized features used in this study

Polarized features DefinitionThe Backscattering coefficient 1205900ℎℎ = ⟨119878ℎℎ119878

lowastℎℎ⟩ 120590

0VV = ⟨119878VV119878

lowastVV⟩ 1205900ℎV = ⟨119878ℎV119878

lowastℎV⟩

Entropy 119867 = sum3119894=1 minus119901119894log3119901119894 (119901119894 = 120582119894sum3119895=1 120582119895)

Scattering angle 120572 = p11205721 + p21205722 + p31205723Anisotropy A = (1205822 minus 1205823) (1205822 + 1205823)Double bounce scattering component 119875119889119887119897 = 119891119889119887119897(1 + |120572|

2)

Odd bounce scattering component 119875119900119889119889 = 119891119900119889119889(1 +100381610038161003816100381612057310038161003816100381610038162)

Volume scattering component 119875V119900119897 = (83) 119891V119900119897

bounce scattering component 119875119900119889119889 and volume scatteringcomponent 119875V119900119897

Notably the 119878ℎℎ 119878ℎV and 119878VV are the elements of Sinclairmatrix 120582i is the i-th eigenvalue of coherence matrix [119879]120572i is derived from the i-th eigenvalue of coherence matrix[119879] 119891119889119887119897 119891119900119889119889 and 119891V119900119897 are double bounce scattering surfacescattering and volume scattering coefficients respectively120572 and 120573 are the processed variables used to describe therelative advantages between different scattering mechanismslt gt represents ensemble average lowast represents conjugateoperation and Re denotes real part

In order to precisely select the optimal polarizationfeature or subset radiometric calibration geometric re-projection and filtering de-noising operations were per-formed on raw SAR data Besides two polarization decom-position algorithms including Freeman decomposition andHAAlpha decomposition were applied for collection ofpolarization features which are listed in Table 2 Noteworthyall polarization features were operated under same size ofde-noising window (3 times 3) and same coordinate system toguarantee registration in unified scaleTheRGB pseudo colorimageries are shown in Figure 4

Quantitative analysis of each polarization feature wasalso necessary for selection of optimal polarization featureor subset With the objective of discriminating four typesof wetland coverage each feature listed in Table 2 wasinvestigated into change curves shown in Figure 5 Figure 5(a)exhibits that the NRCS variations of HH HV and VV areslightly obvious but not enough for precisely differentiatealpine tundra alpine meadow swamp meadow and lakesFor the abovementioned four types of coverages the biggestbiases of NRCS are 264 176 and 214 dB respectively forHH HV and VV polarization Moreover the Freeman andHAAlpha decomposition components were also proposedin this section Noteworthy the decomposition componentswere normalized by using (1) to keep all parameters in samescale

119865119894 =119865isum3i=1 119865119894

(1)

Notably F119894 is the polarized features derived fromFreemanand HAAlpha decomposition

Figure 5(b) demonstrates that the performance of 120572 canwell differentiate lake swamp meadow and alpine meadowbut not alpine tundra and alpinemeadowTheperformance ofboth H and 119860 shows similar curves for lake swamp meadowalpine meadow and alpine tundra and the characteristicof curves are not as clear as 120572 Figure 5(c) represents theFreeman decomposition component A sharp curve of 119875119889119887119897represents the dominance of double scattering mechanism inclassifying the coverages of the Longbao protected plateauwetland Although small bias exists in the performance of119875119900119889119889 and 119875V119900119897 it is still satisfying enough to differentiate lakeswamp meadow alpine meadow and alpine meadow witheither polarized feature Considering the impact of polarizedfeatures in Longbao protected plateau wetland mapping itwas suggested to adopt the polarized features 119875119889119887119897 119875V119900119897 and119875V119900119897 from Freeman decomposition as main factors for furtherclassification input

32 Maximum Likelihood Classification Herein the selectedpolarization parameters were measured for Longbao pro-tected plateau wetland coverage type classification In consid-eration of robustness of classifier the method of MaximumLikelihood Classification (MLC) is widely applied in polar-ized SAR classification [18 19] In this study the MLC wasselected for classification evaluation

The steps for selected polarization features for Longbaoplateau wetland classification performance are listed as fol-lows(1) 80000 pixels are randomly selected as training input

of classifier among which four typical Longbao protectedplateau wetland coverage includes alpine tundra alpinemeadow swamp meadow and lakes(2)The selected pixels are input into classifier with each

tag of coverage type The iteration process stops until thespecified pixel is classified into a certain types of alpinetundra alpine meadow swamp meadow and lakes(3) Another 80000 pixels are used for verification of

classification performanceOwing to absence of in situ surveydata the high resolution GF-2 optical imageries are used

4 Journal of Sensors

Figure 2The in situ survey route for Longbao protected plateau wetland

alpine tundra alpine meadow swamp meadow lakes

Figure 3 Types of Longbao protected plateau wetland coverage

(a) (b) (c)

Figure 4 Pseudo colour imageries of raw polarization channel Freeman decomposition and HAAlpha decomposition results (a) R 120590ℎℎG 120590VV B 120590ℎV (b) R 119867 G119860 B 120572 (c) R119875119889119887119897 G119875119900119889119889 B119875V119900119897

as reference data Moreover the assessment index of Kappacoefficient and average classification accuracy can be derivedfrom confusion matrix(4) Operations are repeated for all three scenes of GF-

3 SAR imageries Based on the proposed Kappa coefficientand average classification accuracy the evaluation of GF-3quad-polarized SAR imagery for Longbao protected plateauwetland classification can be provided

Following the abovementioned steps the classificationmapping is shown in Figure 6 Brown area represents thelakes Yellow area represents the swamp meadow whichdistributes over the lakesides Green area represents thealpine meadow and blue area represents the alpine tundraFor further evaluation of the classification performance theconfusion matrix was derived and Kappa coefficient andmapping accuracy are presented in Table 4 Clearly the lake(accuracy = 975) is easily distinguished from other threecoverage types compared to the alpine tundra (accuracy =

9166)The accuracy of swampmeadow and alpinemeadowis 5350 and 6724 respectively which can be attributedto the similar geometric structure of these two coveragetypes All in all the overall accuracy is 821592 and kappacoefficient is 07517

4 Discussion

Although the classification result of GF-3 quad-polarizedSAR imagery for Longbao protected plateauwetland providessatisfying evaluation a lot more systematic explorations aredemanded in future study

(1) Calibration Precision According to the calibration formulaof equation (5) the NRCS of HH HV VV is -2682 -3803and -2763 dB respectively However the noise equivalentsigma zero (NESZ) of RADARSAT-2 standard quad-pol datais -32 dB Considering the design parameters of GF-3 SAR

Journal of Sensors 5

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

NRC

S d

B

minus24minus26minus28minus30minus32minus34minus36minus38minus40

HHHVVV

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

Ratio

of n

orm

aliz

ed p

aram

eter 034

032

03028026

024022

02

EntropyAnisotropyAlpha

08070605040302

001

Ratio

of n

orm

aliz

ed p

aram

eter

DblOddVol

Figure 5 Polarized features response curves for different types of Longbao protected plateau wetland coverage

Table 4 Classification performance of Longbao protected plateau wetland in 2018-07-05

Ground Truth ()Class Lake Swamp meadow Alpine meadow Alpine tundra TotalLake 9750 000 000 003 3893Swamp meadow 000 5350 306 028 1075Alpine meadow 000 2374 6724 803 1900Alpine tundra 250 2276 2970 9166 3132Total 10000 10000 10000 10000 10000Overall Accuracy = 821592Kappa Coefficient = 07517

6 Journal of Sensors

Alpine tundraAlpine meadowSwamp meadowLakes

33∘14N

33∘11N

33∘10N

33∘14N

33∘11N

33∘10N

96∘30

E 96∘32

E 96∘34

E

96∘30

E 96∘32

E 96∘34

E

Figure 6 Classification mapping of Longbao protected plateau wetland in 2018-07-05

there is a need to discuss and re-adjust the calibrationaccuracy of each polarization channel in particular for HVchannel

(2) Absence of Geometric Parameter of Coverages TypesOwing to the absence of geometric parameter of coveragestypes in particular the plants height above ground orwater body lake or swamp water level and so on onlyquantitative classification accuracy of lake swamp meadowalpine meadow and alpine tundra are given In future study adetailed in situ survey will be carried out for synchronizationacquisition of satellite-earth information The difference ofpolarized features for different coverage types will be relatedto collected ground information More polarized scatteringcharacteristics will be revealed in order to improve the clas-sification performance of Longbao protected plateau wetlandwith GF-3 quad-polarized SAR

5 Conclusion

This study proposed a preliminary evaluation of GF-3 quad-polarized SAR imagery for Longbao protected plateau wet-land classification The main conclusions are as follows Theimaging capability of GF-3 quad-polarized SAR for Long-bao protected plateau wetland was assessed from collecteddataset Beside NRCS the Freeman and HAAlpha decom-position components were also investigated Among all listedpolarized features the double bounce scattering component119875119889119887119897 double bounce scattering component 119875119900119889119889 and volumescattering component 119875V119900119897 from Freeman decompositionalgorithm dominated in distinguishing typical coverage ofLongbao protected plateau wetland Based on MLC theKappa coefficient and average accuracy of Longbao protectedplateau wetland classification mapping were 075 and 8216respectively It was thus verified that the GF-3 quad-polarized

Journal of Sensors 7

SAR can provide a satisfying mapping for wetland classifica-tion

Data Availability

The remote sensing data used to support the findingsof this study have been deposited in the China Centerfor Resources Satellite Data and Application repository(httpwwwcresdacomCN)

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors gratefully acknowledge the financial supportfrom the National Key Research and Development Programof China (2016YFB0502504 2016YFB0502500) and NationalScience Foundation of China (Grant nos 41431174 61471358and 41401427)This article is partly sponsored by the Fundingof Scholarship of Chinese Academy of Sciences (Grant noY8SY1400CX)

References

[1] C Liquete G Zulian I Delgado A Stips and J MaesldquoAssessment of coastal protection as an ecosystem service inEuroperdquo Ecological Indicators vol 30 pp 205ndash217 2013

[2] J Brenner J A Jimenez R Sarda and A Garola ldquoAnassessment of the non-market value of the ecosystem servicesprovided by the Catalan coastal zone Spainrdquo Ocean amp CoastalManagement vol 53 no 1 pp 27ndash38 2010

[3] K Pike P Wright B Wink and S Fletcher ldquoThe assessment ofcultural ecosystem services in the marine environment using Qmethodologyrdquo Journal of Coastal Conservation vol 19 no 5 pp667ndash675 2015

[4] A Niedermeier E Romaneeıen and S Lehner ldquoDetectionof coastlines in SAR images using wavelet methodsrdquo IEEETransactions on Geoscience and Remote Sensing vol 38 no 5pp 2270ndash2281 2000

[5] B R Rao R S Dwivedi S P Kushwaha S N BhattacharyaJ B Anand and S Dasgupta ldquoMonitoring the spatial extent ofcoastal wetlands using ERS-1 SAR datardquo International Journal ofRemote Sensing vol 20 no 13 pp 2509ndash2517 2010

[6] X Ding F Nunziata X Li and M Migliaccio ldquoPerformanceanalysis and validation of waterline extraction approachesusing single- and dual-polarimetric SAR datardquo IEEE Journalof Selected Topics in Applied Earth Observations and RemoteSensing vol 8 no 3 pp 1019ndash1027 2015

[7] A Al Fugura L Billa and B Pradhan ldquoSemi-automatedprocedures for shoreline extraction using single RADARSAT-1 SAR imagerdquo Estuarine Coastal and Shelf Science vol 95 no4 pp 395ndash400 2011

[8] L Ge X Li F Wu and I L Turner ldquoCoastal erosion mappingthrough intergration of SAR and Landsat TM imageryrdquo inProceedings of the 2013 33rd IEEE International Geoscienceand Remote Sensing Symposium IGARSS 2013 pp 2266ndash2269Australia July 2013

[9] M F Bruno M G Molfetta M Mossa R Nutricato AMorea and M T Chiaradia ldquoCoastal observation throughcosmo-skymed high-resolution SAR imagesrdquo Journal of CoastalResearch vol 75 no sp1 pp 795ndash799 2016

[10] S Gou X Li andX Yang ldquoCoastal zone classificationwith fullypolarimetric SAR imageryrdquo IEEE Geoscience amp Remote SensingLetters vol 13 pp 1ndash5 2016

[11] A Buono F Nunziata MMigliaccio X Yang and X Li ldquoClas-sification of the Yellow River delta area using fully polarimetricSAR measurementsrdquo International Journal of Remote Sensingvol 38 no 23 pp 6714ndash6734 2017

[12] W Chen S Gou X Wang X Li and L Jiao ldquoClassification ofPolSAR images using multilayer autoencoders and a self-pacedlearning approachrdquo Remote Sensing vol 10 no 1 2018

[13] WWang X Yang X Li et al ldquoA fully polarimetric SAR imageryclassification scheme for mud and sand flats in intertidal zonesrdquoIEEE Transactions on Geoscience and Remote Sensing vol 55no 3 pp 1734ndash1742 2017

[14] Q Zhang and Y Liu ldquoOverview of chinese first C band multi-polarization SAR satellite GF-3rdquoAerospace China vol 18 no 32017

[15] Y Chang P Li J Yang et al ldquoPolarimetric calibration andquality assessment of the gf-3 satellite images[J]rdquo Sensors vol18 no 2 p 403 2018

[16] M An Q Sun J Hu Y Tang and Z Zhu ldquoCoastline detectionwith gaofen-3 SAR images using an improved FCM methodrdquoSensors vol 18 no 6 article no 1898 p 431 2018

[17] Y Guo E Chen Y Guo Z Li C Li and K Xu ldquoDeep highwayunit network for land cover type classification with GF-3 SARimageryrdquo in Proceedings of the 2017 SAR in Big Data EraModelsMethods and Applications BIGSARDATA 2017 pp 1ndash6 ChinaNovember 2017

[18] M SMajd E Simonetto andL Polidori ldquoMaximum likelihoodclassification of single highresolution polarimetric SAR Imagesin urban areasrdquo Photogrammetrie Fernerkundung Geoinforma-tion vol 2012 no 4 pp 395ndash407 2012

[19] J R Otukei T Blaschke and M Collins ldquoUsing the Wishartmaximum likelihood classifier for assessing the potential ofTerraSAR-X and ALOS PALSAR data for land cover mappingrdquoInternational Journal of Image and Data Fusion vol 5 no 2 pp138ndash151 2014

4 Journal of Sensors

Figure 2The in situ survey route for Longbao protected plateau wetland

alpine tundra alpine meadow swamp meadow lakes

Figure 3 Types of Longbao protected plateau wetland coverage

(a) (b) (c)

Figure 4 Pseudo colour imageries of raw polarization channel Freeman decomposition and HAAlpha decomposition results (a) R 120590ℎℎG 120590VV B 120590ℎV (b) R 119867 G119860 B 120572 (c) R119875119889119887119897 G119875119900119889119889 B119875V119900119897

as reference data Moreover the assessment index of Kappacoefficient and average classification accuracy can be derivedfrom confusion matrix(4) Operations are repeated for all three scenes of GF-

3 SAR imageries Based on the proposed Kappa coefficientand average classification accuracy the evaluation of GF-3quad-polarized SAR imagery for Longbao protected plateauwetland classification can be provided

Following the abovementioned steps the classificationmapping is shown in Figure 6 Brown area represents thelakes Yellow area represents the swamp meadow whichdistributes over the lakesides Green area represents thealpine meadow and blue area represents the alpine tundraFor further evaluation of the classification performance theconfusion matrix was derived and Kappa coefficient andmapping accuracy are presented in Table 4 Clearly the lake(accuracy = 975) is easily distinguished from other threecoverage types compared to the alpine tundra (accuracy =

9166)The accuracy of swampmeadow and alpinemeadowis 5350 and 6724 respectively which can be attributedto the similar geometric structure of these two coveragetypes All in all the overall accuracy is 821592 and kappacoefficient is 07517

4 Discussion

Although the classification result of GF-3 quad-polarizedSAR imagery for Longbao protected plateauwetland providessatisfying evaluation a lot more systematic explorations aredemanded in future study

(1) Calibration Precision According to the calibration formulaof equation (5) the NRCS of HH HV VV is -2682 -3803and -2763 dB respectively However the noise equivalentsigma zero (NESZ) of RADARSAT-2 standard quad-pol datais -32 dB Considering the design parameters of GF-3 SAR

Journal of Sensors 5

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

NRC

S d

B

minus24minus26minus28minus30minus32minus34minus36minus38minus40

HHHVVV

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

Ratio

of n

orm

aliz

ed p

aram

eter 034

032

03028026

024022

02

EntropyAnisotropyAlpha

08070605040302

001

Ratio

of n

orm

aliz

ed p

aram

eter

DblOddVol

Figure 5 Polarized features response curves for different types of Longbao protected plateau wetland coverage

Table 4 Classification performance of Longbao protected plateau wetland in 2018-07-05

Ground Truth ()Class Lake Swamp meadow Alpine meadow Alpine tundra TotalLake 9750 000 000 003 3893Swamp meadow 000 5350 306 028 1075Alpine meadow 000 2374 6724 803 1900Alpine tundra 250 2276 2970 9166 3132Total 10000 10000 10000 10000 10000Overall Accuracy = 821592Kappa Coefficient = 07517

6 Journal of Sensors

Alpine tundraAlpine meadowSwamp meadowLakes

33∘14N

33∘11N

33∘10N

33∘14N

33∘11N

33∘10N

96∘30

E 96∘32

E 96∘34

E

96∘30

E 96∘32

E 96∘34

E

Figure 6 Classification mapping of Longbao protected plateau wetland in 2018-07-05

there is a need to discuss and re-adjust the calibrationaccuracy of each polarization channel in particular for HVchannel

(2) Absence of Geometric Parameter of Coverages TypesOwing to the absence of geometric parameter of coveragestypes in particular the plants height above ground orwater body lake or swamp water level and so on onlyquantitative classification accuracy of lake swamp meadowalpine meadow and alpine tundra are given In future study adetailed in situ survey will be carried out for synchronizationacquisition of satellite-earth information The difference ofpolarized features for different coverage types will be relatedto collected ground information More polarized scatteringcharacteristics will be revealed in order to improve the clas-sification performance of Longbao protected plateau wetlandwith GF-3 quad-polarized SAR

5 Conclusion

This study proposed a preliminary evaluation of GF-3 quad-polarized SAR imagery for Longbao protected plateau wet-land classification The main conclusions are as follows Theimaging capability of GF-3 quad-polarized SAR for Long-bao protected plateau wetland was assessed from collecteddataset Beside NRCS the Freeman and HAAlpha decom-position components were also investigated Among all listedpolarized features the double bounce scattering component119875119889119887119897 double bounce scattering component 119875119900119889119889 and volumescattering component 119875V119900119897 from Freeman decompositionalgorithm dominated in distinguishing typical coverage ofLongbao protected plateau wetland Based on MLC theKappa coefficient and average accuracy of Longbao protectedplateau wetland classification mapping were 075 and 8216respectively It was thus verified that the GF-3 quad-polarized

Journal of Sensors 7

SAR can provide a satisfying mapping for wetland classifica-tion

Data Availability

The remote sensing data used to support the findingsof this study have been deposited in the China Centerfor Resources Satellite Data and Application repository(httpwwwcresdacomCN)

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors gratefully acknowledge the financial supportfrom the National Key Research and Development Programof China (2016YFB0502504 2016YFB0502500) and NationalScience Foundation of China (Grant nos 41431174 61471358and 41401427)This article is partly sponsored by the Fundingof Scholarship of Chinese Academy of Sciences (Grant noY8SY1400CX)

References

[1] C Liquete G Zulian I Delgado A Stips and J MaesldquoAssessment of coastal protection as an ecosystem service inEuroperdquo Ecological Indicators vol 30 pp 205ndash217 2013

[2] J Brenner J A Jimenez R Sarda and A Garola ldquoAnassessment of the non-market value of the ecosystem servicesprovided by the Catalan coastal zone Spainrdquo Ocean amp CoastalManagement vol 53 no 1 pp 27ndash38 2010

[3] K Pike P Wright B Wink and S Fletcher ldquoThe assessment ofcultural ecosystem services in the marine environment using Qmethodologyrdquo Journal of Coastal Conservation vol 19 no 5 pp667ndash675 2015

[4] A Niedermeier E Romaneeıen and S Lehner ldquoDetectionof coastlines in SAR images using wavelet methodsrdquo IEEETransactions on Geoscience and Remote Sensing vol 38 no 5pp 2270ndash2281 2000

[5] B R Rao R S Dwivedi S P Kushwaha S N BhattacharyaJ B Anand and S Dasgupta ldquoMonitoring the spatial extent ofcoastal wetlands using ERS-1 SAR datardquo International Journal ofRemote Sensing vol 20 no 13 pp 2509ndash2517 2010

[6] X Ding F Nunziata X Li and M Migliaccio ldquoPerformanceanalysis and validation of waterline extraction approachesusing single- and dual-polarimetric SAR datardquo IEEE Journalof Selected Topics in Applied Earth Observations and RemoteSensing vol 8 no 3 pp 1019ndash1027 2015

[7] A Al Fugura L Billa and B Pradhan ldquoSemi-automatedprocedures for shoreline extraction using single RADARSAT-1 SAR imagerdquo Estuarine Coastal and Shelf Science vol 95 no4 pp 395ndash400 2011

[8] L Ge X Li F Wu and I L Turner ldquoCoastal erosion mappingthrough intergration of SAR and Landsat TM imageryrdquo inProceedings of the 2013 33rd IEEE International Geoscienceand Remote Sensing Symposium IGARSS 2013 pp 2266ndash2269Australia July 2013

[9] M F Bruno M G Molfetta M Mossa R Nutricato AMorea and M T Chiaradia ldquoCoastal observation throughcosmo-skymed high-resolution SAR imagesrdquo Journal of CoastalResearch vol 75 no sp1 pp 795ndash799 2016

[10] S Gou X Li andX Yang ldquoCoastal zone classificationwith fullypolarimetric SAR imageryrdquo IEEE Geoscience amp Remote SensingLetters vol 13 pp 1ndash5 2016

[11] A Buono F Nunziata MMigliaccio X Yang and X Li ldquoClas-sification of the Yellow River delta area using fully polarimetricSAR measurementsrdquo International Journal of Remote Sensingvol 38 no 23 pp 6714ndash6734 2017

[12] W Chen S Gou X Wang X Li and L Jiao ldquoClassification ofPolSAR images using multilayer autoencoders and a self-pacedlearning approachrdquo Remote Sensing vol 10 no 1 2018

[13] WWang X Yang X Li et al ldquoA fully polarimetric SAR imageryclassification scheme for mud and sand flats in intertidal zonesrdquoIEEE Transactions on Geoscience and Remote Sensing vol 55no 3 pp 1734ndash1742 2017

[14] Q Zhang and Y Liu ldquoOverview of chinese first C band multi-polarization SAR satellite GF-3rdquoAerospace China vol 18 no 32017

[15] Y Chang P Li J Yang et al ldquoPolarimetric calibration andquality assessment of the gf-3 satellite images[J]rdquo Sensors vol18 no 2 p 403 2018

[16] M An Q Sun J Hu Y Tang and Z Zhu ldquoCoastline detectionwith gaofen-3 SAR images using an improved FCM methodrdquoSensors vol 18 no 6 article no 1898 p 431 2018

[17] Y Guo E Chen Y Guo Z Li C Li and K Xu ldquoDeep highwayunit network for land cover type classification with GF-3 SARimageryrdquo in Proceedings of the 2017 SAR in Big Data EraModelsMethods and Applications BIGSARDATA 2017 pp 1ndash6 ChinaNovember 2017

[18] M SMajd E Simonetto andL Polidori ldquoMaximum likelihoodclassification of single highresolution polarimetric SAR Imagesin urban areasrdquo Photogrammetrie Fernerkundung Geoinforma-tion vol 2012 no 4 pp 395ndash407 2012

[19] J R Otukei T Blaschke and M Collins ldquoUsing the Wishartmaximum likelihood classifier for assessing the potential ofTerraSAR-X and ALOS PALSAR data for land cover mappingrdquoInternational Journal of Image and Data Fusion vol 5 no 2 pp138ndash151 2014

Journal of Sensors 5

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

NRC

S d

B

minus24minus26minus28minus30minus32minus34minus36minus38minus40

HHHVVV

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

alpine tundraalpine meadowswamp meadowlakesTypes of Longbao wetland coverage

Ratio

of n

orm

aliz

ed p

aram

eter 034

032

03028026

024022

02

EntropyAnisotropyAlpha

08070605040302

001

Ratio

of n

orm

aliz

ed p

aram

eter

DblOddVol

Figure 5 Polarized features response curves for different types of Longbao protected plateau wetland coverage

Table 4 Classification performance of Longbao protected plateau wetland in 2018-07-05

Ground Truth ()Class Lake Swamp meadow Alpine meadow Alpine tundra TotalLake 9750 000 000 003 3893Swamp meadow 000 5350 306 028 1075Alpine meadow 000 2374 6724 803 1900Alpine tundra 250 2276 2970 9166 3132Total 10000 10000 10000 10000 10000Overall Accuracy = 821592Kappa Coefficient = 07517

6 Journal of Sensors

Alpine tundraAlpine meadowSwamp meadowLakes

33∘14N

33∘11N

33∘10N

33∘14N

33∘11N

33∘10N

96∘30

E 96∘32

E 96∘34

E

96∘30

E 96∘32

E 96∘34

E

Figure 6 Classification mapping of Longbao protected plateau wetland in 2018-07-05

there is a need to discuss and re-adjust the calibrationaccuracy of each polarization channel in particular for HVchannel

(2) Absence of Geometric Parameter of Coverages TypesOwing to the absence of geometric parameter of coveragestypes in particular the plants height above ground orwater body lake or swamp water level and so on onlyquantitative classification accuracy of lake swamp meadowalpine meadow and alpine tundra are given In future study adetailed in situ survey will be carried out for synchronizationacquisition of satellite-earth information The difference ofpolarized features for different coverage types will be relatedto collected ground information More polarized scatteringcharacteristics will be revealed in order to improve the clas-sification performance of Longbao protected plateau wetlandwith GF-3 quad-polarized SAR

5 Conclusion

This study proposed a preliminary evaluation of GF-3 quad-polarized SAR imagery for Longbao protected plateau wet-land classification The main conclusions are as follows Theimaging capability of GF-3 quad-polarized SAR for Long-bao protected plateau wetland was assessed from collecteddataset Beside NRCS the Freeman and HAAlpha decom-position components were also investigated Among all listedpolarized features the double bounce scattering component119875119889119887119897 double bounce scattering component 119875119900119889119889 and volumescattering component 119875V119900119897 from Freeman decompositionalgorithm dominated in distinguishing typical coverage ofLongbao protected plateau wetland Based on MLC theKappa coefficient and average accuracy of Longbao protectedplateau wetland classification mapping were 075 and 8216respectively It was thus verified that the GF-3 quad-polarized

Journal of Sensors 7

SAR can provide a satisfying mapping for wetland classifica-tion

Data Availability

The remote sensing data used to support the findingsof this study have been deposited in the China Centerfor Resources Satellite Data and Application repository(httpwwwcresdacomCN)

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors gratefully acknowledge the financial supportfrom the National Key Research and Development Programof China (2016YFB0502504 2016YFB0502500) and NationalScience Foundation of China (Grant nos 41431174 61471358and 41401427)This article is partly sponsored by the Fundingof Scholarship of Chinese Academy of Sciences (Grant noY8SY1400CX)

References

[1] C Liquete G Zulian I Delgado A Stips and J MaesldquoAssessment of coastal protection as an ecosystem service inEuroperdquo Ecological Indicators vol 30 pp 205ndash217 2013

[2] J Brenner J A Jimenez R Sarda and A Garola ldquoAnassessment of the non-market value of the ecosystem servicesprovided by the Catalan coastal zone Spainrdquo Ocean amp CoastalManagement vol 53 no 1 pp 27ndash38 2010

[3] K Pike P Wright B Wink and S Fletcher ldquoThe assessment ofcultural ecosystem services in the marine environment using Qmethodologyrdquo Journal of Coastal Conservation vol 19 no 5 pp667ndash675 2015

[4] A Niedermeier E Romaneeıen and S Lehner ldquoDetectionof coastlines in SAR images using wavelet methodsrdquo IEEETransactions on Geoscience and Remote Sensing vol 38 no 5pp 2270ndash2281 2000

[5] B R Rao R S Dwivedi S P Kushwaha S N BhattacharyaJ B Anand and S Dasgupta ldquoMonitoring the spatial extent ofcoastal wetlands using ERS-1 SAR datardquo International Journal ofRemote Sensing vol 20 no 13 pp 2509ndash2517 2010

[6] X Ding F Nunziata X Li and M Migliaccio ldquoPerformanceanalysis and validation of waterline extraction approachesusing single- and dual-polarimetric SAR datardquo IEEE Journalof Selected Topics in Applied Earth Observations and RemoteSensing vol 8 no 3 pp 1019ndash1027 2015

[7] A Al Fugura L Billa and B Pradhan ldquoSemi-automatedprocedures for shoreline extraction using single RADARSAT-1 SAR imagerdquo Estuarine Coastal and Shelf Science vol 95 no4 pp 395ndash400 2011

[8] L Ge X Li F Wu and I L Turner ldquoCoastal erosion mappingthrough intergration of SAR and Landsat TM imageryrdquo inProceedings of the 2013 33rd IEEE International Geoscienceand Remote Sensing Symposium IGARSS 2013 pp 2266ndash2269Australia July 2013

[9] M F Bruno M G Molfetta M Mossa R Nutricato AMorea and M T Chiaradia ldquoCoastal observation throughcosmo-skymed high-resolution SAR imagesrdquo Journal of CoastalResearch vol 75 no sp1 pp 795ndash799 2016

[10] S Gou X Li andX Yang ldquoCoastal zone classificationwith fullypolarimetric SAR imageryrdquo IEEE Geoscience amp Remote SensingLetters vol 13 pp 1ndash5 2016

[11] A Buono F Nunziata MMigliaccio X Yang and X Li ldquoClas-sification of the Yellow River delta area using fully polarimetricSAR measurementsrdquo International Journal of Remote Sensingvol 38 no 23 pp 6714ndash6734 2017

[12] W Chen S Gou X Wang X Li and L Jiao ldquoClassification ofPolSAR images using multilayer autoencoders and a self-pacedlearning approachrdquo Remote Sensing vol 10 no 1 2018

[13] WWang X Yang X Li et al ldquoA fully polarimetric SAR imageryclassification scheme for mud and sand flats in intertidal zonesrdquoIEEE Transactions on Geoscience and Remote Sensing vol 55no 3 pp 1734ndash1742 2017

[14] Q Zhang and Y Liu ldquoOverview of chinese first C band multi-polarization SAR satellite GF-3rdquoAerospace China vol 18 no 32017

[15] Y Chang P Li J Yang et al ldquoPolarimetric calibration andquality assessment of the gf-3 satellite images[J]rdquo Sensors vol18 no 2 p 403 2018

[16] M An Q Sun J Hu Y Tang and Z Zhu ldquoCoastline detectionwith gaofen-3 SAR images using an improved FCM methodrdquoSensors vol 18 no 6 article no 1898 p 431 2018

[17] Y Guo E Chen Y Guo Z Li C Li and K Xu ldquoDeep highwayunit network for land cover type classification with GF-3 SARimageryrdquo in Proceedings of the 2017 SAR in Big Data EraModelsMethods and Applications BIGSARDATA 2017 pp 1ndash6 ChinaNovember 2017

[18] M SMajd E Simonetto andL Polidori ldquoMaximum likelihoodclassification of single highresolution polarimetric SAR Imagesin urban areasrdquo Photogrammetrie Fernerkundung Geoinforma-tion vol 2012 no 4 pp 395ndash407 2012

[19] J R Otukei T Blaschke and M Collins ldquoUsing the Wishartmaximum likelihood classifier for assessing the potential ofTerraSAR-X and ALOS PALSAR data for land cover mappingrdquoInternational Journal of Image and Data Fusion vol 5 no 2 pp138ndash151 2014

6 Journal of Sensors

Alpine tundraAlpine meadowSwamp meadowLakes

33∘14N

33∘11N

33∘10N

33∘14N

33∘11N

33∘10N

96∘30

E 96∘32

E 96∘34

E

96∘30

E 96∘32

E 96∘34

E

Figure 6 Classification mapping of Longbao protected plateau wetland in 2018-07-05

there is a need to discuss and re-adjust the calibrationaccuracy of each polarization channel in particular for HVchannel

(2) Absence of Geometric Parameter of Coverages TypesOwing to the absence of geometric parameter of coveragestypes in particular the plants height above ground orwater body lake or swamp water level and so on onlyquantitative classification accuracy of lake swamp meadowalpine meadow and alpine tundra are given In future study adetailed in situ survey will be carried out for synchronizationacquisition of satellite-earth information The difference ofpolarized features for different coverage types will be relatedto collected ground information More polarized scatteringcharacteristics will be revealed in order to improve the clas-sification performance of Longbao protected plateau wetlandwith GF-3 quad-polarized SAR

5 Conclusion

This study proposed a preliminary evaluation of GF-3 quad-polarized SAR imagery for Longbao protected plateau wet-land classification The main conclusions are as follows Theimaging capability of GF-3 quad-polarized SAR for Long-bao protected plateau wetland was assessed from collecteddataset Beside NRCS the Freeman and HAAlpha decom-position components were also investigated Among all listedpolarized features the double bounce scattering component119875119889119887119897 double bounce scattering component 119875119900119889119889 and volumescattering component 119875V119900119897 from Freeman decompositionalgorithm dominated in distinguishing typical coverage ofLongbao protected plateau wetland Based on MLC theKappa coefficient and average accuracy of Longbao protectedplateau wetland classification mapping were 075 and 8216respectively It was thus verified that the GF-3 quad-polarized

Journal of Sensors 7

SAR can provide a satisfying mapping for wetland classifica-tion

Data Availability

The remote sensing data used to support the findingsof this study have been deposited in the China Centerfor Resources Satellite Data and Application repository(httpwwwcresdacomCN)

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors gratefully acknowledge the financial supportfrom the National Key Research and Development Programof China (2016YFB0502504 2016YFB0502500) and NationalScience Foundation of China (Grant nos 41431174 61471358and 41401427)This article is partly sponsored by the Fundingof Scholarship of Chinese Academy of Sciences (Grant noY8SY1400CX)

References

[1] C Liquete G Zulian I Delgado A Stips and J MaesldquoAssessment of coastal protection as an ecosystem service inEuroperdquo Ecological Indicators vol 30 pp 205ndash217 2013

[2] J Brenner J A Jimenez R Sarda and A Garola ldquoAnassessment of the non-market value of the ecosystem servicesprovided by the Catalan coastal zone Spainrdquo Ocean amp CoastalManagement vol 53 no 1 pp 27ndash38 2010

[3] K Pike P Wright B Wink and S Fletcher ldquoThe assessment ofcultural ecosystem services in the marine environment using Qmethodologyrdquo Journal of Coastal Conservation vol 19 no 5 pp667ndash675 2015

[4] A Niedermeier E Romaneeıen and S Lehner ldquoDetectionof coastlines in SAR images using wavelet methodsrdquo IEEETransactions on Geoscience and Remote Sensing vol 38 no 5pp 2270ndash2281 2000

[5] B R Rao R S Dwivedi S P Kushwaha S N BhattacharyaJ B Anand and S Dasgupta ldquoMonitoring the spatial extent ofcoastal wetlands using ERS-1 SAR datardquo International Journal ofRemote Sensing vol 20 no 13 pp 2509ndash2517 2010

[6] X Ding F Nunziata X Li and M Migliaccio ldquoPerformanceanalysis and validation of waterline extraction approachesusing single- and dual-polarimetric SAR datardquo IEEE Journalof Selected Topics in Applied Earth Observations and RemoteSensing vol 8 no 3 pp 1019ndash1027 2015

[7] A Al Fugura L Billa and B Pradhan ldquoSemi-automatedprocedures for shoreline extraction using single RADARSAT-1 SAR imagerdquo Estuarine Coastal and Shelf Science vol 95 no4 pp 395ndash400 2011

[8] L Ge X Li F Wu and I L Turner ldquoCoastal erosion mappingthrough intergration of SAR and Landsat TM imageryrdquo inProceedings of the 2013 33rd IEEE International Geoscienceand Remote Sensing Symposium IGARSS 2013 pp 2266ndash2269Australia July 2013

[9] M F Bruno M G Molfetta M Mossa R Nutricato AMorea and M T Chiaradia ldquoCoastal observation throughcosmo-skymed high-resolution SAR imagesrdquo Journal of CoastalResearch vol 75 no sp1 pp 795ndash799 2016

[10] S Gou X Li andX Yang ldquoCoastal zone classificationwith fullypolarimetric SAR imageryrdquo IEEE Geoscience amp Remote SensingLetters vol 13 pp 1ndash5 2016

[11] A Buono F Nunziata MMigliaccio X Yang and X Li ldquoClas-sification of the Yellow River delta area using fully polarimetricSAR measurementsrdquo International Journal of Remote Sensingvol 38 no 23 pp 6714ndash6734 2017

[12] W Chen S Gou X Wang X Li and L Jiao ldquoClassification ofPolSAR images using multilayer autoencoders and a self-pacedlearning approachrdquo Remote Sensing vol 10 no 1 2018

[13] WWang X Yang X Li et al ldquoA fully polarimetric SAR imageryclassification scheme for mud and sand flats in intertidal zonesrdquoIEEE Transactions on Geoscience and Remote Sensing vol 55no 3 pp 1734ndash1742 2017

[14] Q Zhang and Y Liu ldquoOverview of chinese first C band multi-polarization SAR satellite GF-3rdquoAerospace China vol 18 no 32017

[15] Y Chang P Li J Yang et al ldquoPolarimetric calibration andquality assessment of the gf-3 satellite images[J]rdquo Sensors vol18 no 2 p 403 2018

[16] M An Q Sun J Hu Y Tang and Z Zhu ldquoCoastline detectionwith gaofen-3 SAR images using an improved FCM methodrdquoSensors vol 18 no 6 article no 1898 p 431 2018

[17] Y Guo E Chen Y Guo Z Li C Li and K Xu ldquoDeep highwayunit network for land cover type classification with GF-3 SARimageryrdquo in Proceedings of the 2017 SAR in Big Data EraModelsMethods and Applications BIGSARDATA 2017 pp 1ndash6 ChinaNovember 2017

[18] M SMajd E Simonetto andL Polidori ldquoMaximum likelihoodclassification of single highresolution polarimetric SAR Imagesin urban areasrdquo Photogrammetrie Fernerkundung Geoinforma-tion vol 2012 no 4 pp 395ndash407 2012

[19] J R Otukei T Blaschke and M Collins ldquoUsing the Wishartmaximum likelihood classifier for assessing the potential ofTerraSAR-X and ALOS PALSAR data for land cover mappingrdquoInternational Journal of Image and Data Fusion vol 5 no 2 pp138ndash151 2014

Journal of Sensors 7

SAR can provide a satisfying mapping for wetland classifica-tion

Data Availability

The remote sensing data used to support the findingsof this study have been deposited in the China Centerfor Resources Satellite Data and Application repository(httpwwwcresdacomCN)

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors gratefully acknowledge the financial supportfrom the National Key Research and Development Programof China (2016YFB0502504 2016YFB0502500) and NationalScience Foundation of China (Grant nos 41431174 61471358and 41401427)This article is partly sponsored by the Fundingof Scholarship of Chinese Academy of Sciences (Grant noY8SY1400CX)

References

[1] C Liquete G Zulian I Delgado A Stips and J MaesldquoAssessment of coastal protection as an ecosystem service inEuroperdquo Ecological Indicators vol 30 pp 205ndash217 2013

[2] J Brenner J A Jimenez R Sarda and A Garola ldquoAnassessment of the non-market value of the ecosystem servicesprovided by the Catalan coastal zone Spainrdquo Ocean amp CoastalManagement vol 53 no 1 pp 27ndash38 2010

[3] K Pike P Wright B Wink and S Fletcher ldquoThe assessment ofcultural ecosystem services in the marine environment using Qmethodologyrdquo Journal of Coastal Conservation vol 19 no 5 pp667ndash675 2015

[4] A Niedermeier E Romaneeıen and S Lehner ldquoDetectionof coastlines in SAR images using wavelet methodsrdquo IEEETransactions on Geoscience and Remote Sensing vol 38 no 5pp 2270ndash2281 2000

[5] B R Rao R S Dwivedi S P Kushwaha S N BhattacharyaJ B Anand and S Dasgupta ldquoMonitoring the spatial extent ofcoastal wetlands using ERS-1 SAR datardquo International Journal ofRemote Sensing vol 20 no 13 pp 2509ndash2517 2010

[6] X Ding F Nunziata X Li and M Migliaccio ldquoPerformanceanalysis and validation of waterline extraction approachesusing single- and dual-polarimetric SAR datardquo IEEE Journalof Selected Topics in Applied Earth Observations and RemoteSensing vol 8 no 3 pp 1019ndash1027 2015

[7] A Al Fugura L Billa and B Pradhan ldquoSemi-automatedprocedures for shoreline extraction using single RADARSAT-1 SAR imagerdquo Estuarine Coastal and Shelf Science vol 95 no4 pp 395ndash400 2011

[8] L Ge X Li F Wu and I L Turner ldquoCoastal erosion mappingthrough intergration of SAR and Landsat TM imageryrdquo inProceedings of the 2013 33rd IEEE International Geoscienceand Remote Sensing Symposium IGARSS 2013 pp 2266ndash2269Australia July 2013

[9] M F Bruno M G Molfetta M Mossa R Nutricato AMorea and M T Chiaradia ldquoCoastal observation throughcosmo-skymed high-resolution SAR imagesrdquo Journal of CoastalResearch vol 75 no sp1 pp 795ndash799 2016

[10] S Gou X Li andX Yang ldquoCoastal zone classificationwith fullypolarimetric SAR imageryrdquo IEEE Geoscience amp Remote SensingLetters vol 13 pp 1ndash5 2016

[11] A Buono F Nunziata MMigliaccio X Yang and X Li ldquoClas-sification of the Yellow River delta area using fully polarimetricSAR measurementsrdquo International Journal of Remote Sensingvol 38 no 23 pp 6714ndash6734 2017

[12] W Chen S Gou X Wang X Li and L Jiao ldquoClassification ofPolSAR images using multilayer autoencoders and a self-pacedlearning approachrdquo Remote Sensing vol 10 no 1 2018

[13] WWang X Yang X Li et al ldquoA fully polarimetric SAR imageryclassification scheme for mud and sand flats in intertidal zonesrdquoIEEE Transactions on Geoscience and Remote Sensing vol 55no 3 pp 1734ndash1742 2017

[14] Q Zhang and Y Liu ldquoOverview of chinese first C band multi-polarization SAR satellite GF-3rdquoAerospace China vol 18 no 32017

[15] Y Chang P Li J Yang et al ldquoPolarimetric calibration andquality assessment of the gf-3 satellite images[J]rdquo Sensors vol18 no 2 p 403 2018

[16] M An Q Sun J Hu Y Tang and Z Zhu ldquoCoastline detectionwith gaofen-3 SAR images using an improved FCM methodrdquoSensors vol 18 no 6 article no 1898 p 431 2018

[17] Y Guo E Chen Y Guo Z Li C Li and K Xu ldquoDeep highwayunit network for land cover type classification with GF-3 SARimageryrdquo in Proceedings of the 2017 SAR in Big Data EraModelsMethods and Applications BIGSARDATA 2017 pp 1ndash6 ChinaNovember 2017

[18] M SMajd E Simonetto andL Polidori ldquoMaximum likelihoodclassification of single highresolution polarimetric SAR Imagesin urban areasrdquo Photogrammetrie Fernerkundung Geoinforma-tion vol 2012 no 4 pp 395ndash407 2012

[19] J R Otukei T Blaschke and M Collins ldquoUsing the Wishartmaximum likelihood classifier for assessing the potential ofTerraSAR-X and ALOS PALSAR data for land cover mappingrdquoInternational Journal of Image and Data Fusion vol 5 no 2 pp138ndash151 2014

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