presentation on digital elevation model · slope data: james r.f. barringer & linda lilburne,...

Dr. Pratibha Shingare/ Mr. Sumit Kale

College of Engineering Pune

Maharashtra, India

nrsc/I

SRO

This Research work is Sponsored

by

Indian Space Research Organization

&

Supported by

Technical Education Quality

Improvement Program (TEQIP)

nrsc/I

SRO

INTRODUCTIONDigital elevation model is a numerical data file that contains

the elevation of the topography over a specified area, usually at

a fixed grid interval over the surface of the earth.

DEM is used as a tool to represent the earth’s surface in many

applications such as hydrological modeling, precision

agriculture, civil engineering, large-scale mapping &

telecommunication.nrsc/I

SRO

DEM CREATION

I. Conversion of contour lines.

II. Photogrammetry.

III. Radar Stereo.

IV. Radar Inferometry.

V. Laser Altimetry.

nrsc/I

SRO

Photogrammetry Manually: an operator looks at a pair of stereophotos

through a stereoplotter and must move two dots togetheruntil they appear to be one lying just at the surface of theground

Automatically: an instrument calculates the parallaxdisplacement of a large number of points (e.g. for USGS7.5 minute quadrangles, the Gestalt Photo Mapper IIcorrelates 500,000 points)

Correction of elevation from photographs: water bodiesare assumed to be flat

nrsc/I

SRO

WORKFLOW FOR DEM GENERATION

1. Selection of Stereo Pair of Image.

2. Selection of RPCs.

3. Selection of Ground Control points (GCP).

4. Selection of Tie Points.

5. Generation of Epipolar Images.

6. DEM Generation.nrsc/I

SRO

nrsc/I

SRO

Cartosat-1 Stereo Pair details

Image No. 1 (DEM-1) Image No. 2 (DEM-2)

Scene Center Lat= 18.68334103,Scene Center Lon= 73.63460807,Sat Altitude= 622762.1005192,Sun Azimuth= 135.80992901,Sun Elevation= 55.46966731,Satellite Heading= 191.92684200,Angle Incidence= 28.59150863

Scene Center Lat=18.68104922,Scene Center Lon=74.01076521,Sat Altitude=623159.03537973,Sun Azimuth=148.46323039,Sun Elevation=57.00538005,Satellite Heading=191.90596600,Angle Incidence=28.60600143nrs

c/ISRO

AFT Image FORE Image

nrsc/I

SRO

Tie Point & RPC Selection Tie points are pairs of pixels corresponding to the

same locations where images overlap.

They are an integral part of the bundle adjustment and contribute to most of the relative accuracy in the Ortho rectified product.

nrsc/I

SRO

Epipolar Image Generation Epipolar images are stereo pairs in which the left and

right images are oriented in such a way that groundfeature points have the same y-coordinates on bothimages.

Using epipolar images removes one dimension ofvariability, thus greatly increasing the speed of image-matching processing as well as the reliability of thematching results.nrs

c/ISRO

RESULT

DEM-1 DEM-2

nrsc/I

SRO

Quality of DEM data

• Quality of DEM data depends upon:-

– Terrain roughness

– Data Capturing Source (Ex.- Satellite)

– Sampling density (elevation data collection method)

– Grid resolution or pixel size

– Interpolation algorithm

– Vertical resolution nrsc/I

SRO

DEM-1 Parameters

Slope Aspect

nrsc/I

SRO

DEM-2 Parameters

Slope Aspect

nrsc/I

SRO

DEM-1 DEM-2

Test 1Moran's I:

Autocorrelation Index: 0.9994997188

Expected Value, if band is uncorrelated: -

0.000000

Standard Deviation of Expected Value

(Normalized): 0.000062


(Randomized): 0.000062

Z Significance Test (Normalized):

16032.035871

Z Significance Test (Randomized):

16032.035791

Test 1Moran's I:


Expected Value, if band is uncorrelated: -

0.000000






16008.753711


16008.753634

nrsc/I

SRO

DEM-1 DEM-2

Test 2Geary's C:


Expected Value, if band is uncorrelated:

1.000000






16033.125164


16033.575993

Semi variance: 246.7618800980

Test 2Geary's C:


Expected Value, if band is uncorrelated:

1.000000






16009.932265


16010.369337

Semi variance: 229.9913459780

nrsc/I

SRO

From visual Inspection we can sense that DEM-1 have

more hilly areas as compared to DEM-2. Thought we

plotted DEM for same region it shows variations in the

result due to slight change in coordinates & date of pass of

satellite.

Bright spots in both DEMs shows hilly areas.

By analyzing DEM along with Aspect & Slope maps we

can analyze various terrain features like Hills, Rivers,

Valleys, Urbanize areas, Water canopies etc.

ANALYSIS OF EXTRACTED DEM

nrsc/I

SRO

For DEM-1 , Maximum Y parallax error value was 2.3145

while for DEM-2 it was 4.2341. So for DEM-1 it shows

perfect match if tie points as compared to DEM-2.

The maximum permissible Y parallax error in DEM

creation is 10 Pixels. As this error reduces the accuracy of

tie point matching & DEM also increases.

In proposed research work, the DEM produced from

Cartosat-1 comes under category of Relative DEM i. e

DEM without GCPs.nrsc/I

SRO

Test 1 Details:-

Moran’s I value used for determination of spatial

autocorrelation (feature similarity) based on both feature

locations and feature values simultaneously.

In both DEM-1 & DEM-2 it illustrates Clustering of

Patterns. Values near +1 indicates Clustering while near -1

indicates Dispersion of Patterns.

nrsc/I

SRO

Test 2 Details:-

Geary’s C values indicate spatial autocorrelation.

Positive spatial autocorrelation is found with values

ranging from 0 to 1 and negative spatial autocorrelation is

found between 1 and 2.

In both DEM-1 & DEM-2 it shows Positive

Autocorrelation. nrsc/I

SRO

Quality of input data from which the DEM is generated

(stereo pair satellite image in our case) has more

significant effect on DEM accuracy than algorithms used

under Interpolation.

Nearest neighbor method should be used when the data

values cannot be changed, for example, with categorical

data or qualitative data such as soils types.nrsc/I

SRO

Conclusion The proposed research deals with analysis of Relative

DEM generated from Cartosat-1. For more accurate &

smooth DEM, accurate lay out of ground control points

are necessary.

Perfect match of tie points while generating automatic

DEM produce more smooth & accurate DEM.

Maximum Y Parallax Error value should be lie very close

to 0. It shows perfect match of two stereo pair images

while generating DEM.

nrsc/I

SRO

While evaluating accuracy of generated DEM we got

slight difference between generated DEM & reference

source (Toposheet of Pune Area). The difference between

two elevation value comes approximately 5 meters. For

better result & higher Horizontal as well as vertical

accuracy we need DEM with GCPs.

nrsc/I

SRO

Future Aspect Our future aspect is to fuse DEMs of same area generated

from various sources especially Cartosat-1 & RISAT.

We are going to follow methodology of “Sparse

representation fusion” proposed by Papasaika to do

simple fusion of two cartosat-1 DEMs.

For Multiple DEM Fusion generated from multiple

sources we are going to follow “K means clustering

method” proposed by Colleen E. Fuss.nrsc/I

SRO

DEM Application• Commercial applications.

• Industrial applications.

• Operational applications.

• Military applications.

• Climate impact studies.

Many More………………………nrsc/I

SRO

Research Work Published till date

IEEE Conference EMS 2013 Manchester U.K “An Interpretation of Digital Elevation Model Using Cartosat-1 Image”

IJMER – July 2013 edition

“Review on Digital Elevation Model”

nrsc/I

SRO

Reference Digital Terrain Modeling – Principles and Methodology by Zhilin Li, Qing Zhu, Christopher Gold.

Digital Terrain Modeling- Acquisition, Manipulation and Applications by Naser El-Sheimy, CaterinaValeo and Ayman Habib.

T.Ch. Malleshwara Rao, K.V Rao, A.Ravi Kumar, D.P Rao, B.L Deekshatula- DTM from IRS satellitedata from the overlap area of two adjacent paths using Digital Photogrammetric technique NRSC, PE& RS June 1996.

Tomaz Podobnikar Methods for visual quality assessment of a digital terrain model S.A.P.I.E.N.S2.2 | 2009 : Vol.2.

Federal Geographic Data Committee, 1998: Geospatial Position Accuracy Standards Part 3: NationalStandard for Spatial Data Accuracy, FGDC-STD-007.3-1998. Washington, D. C.: Federal GeographicData Committee.

Joint Research Center, ISPRA, 2008. Guidelines for Best Practice and Quality Checking of OrthoImagery, Issue 3.0, v.20/10/2008.

An Evaluation of Digital Elevation Models for Upgrading New Zealand Land Resource InventorySlope Data: James R.F. Barringer & Linda Lilburne, Presented at the second annual conference ofGeoComputation ‘97 & SIRC ‘97, University of Otago, New Zealand, 26-29 August 1997.

Modelling the Continuity of Surface Form Using Digital Elevation Models by Joseph Wood.

ENVI Exelis Documentation Center- http://www.exelisvis.com/docs

An evaluation of methods to determine slope using digital elevation data by S.D. Warren, M.G.Hohmann , K. Auerswald , H. Mitasova, 7 May 2004.

Methods for visual quality assessment of a digital terrain model by Tomaz Podobnikar, 2.2 Vol. 2/ n02Special issue S.A.P.I.E.N.S 2009

nrsc/I

SRO

http://www.exelisvis.com/docs







nrsc/I

SRO

presentation on digital elevation model · slope data: james r.f. barringer & linda lilburne,...

Documents