developing a classification framework for landcover landuse change analysis in chile

KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association

Institute of Photogrammetry and Remote Sensing - IPF

www.kit.edu

Developing a classification framework for landcover landuse change analysis in Chile

Dipl. Geoecologist Andreas Ch. Braun – Karlsruhe Institute of Technology – KIT

Institute of Photogrammetry and Remote Sensing2 22.07.11 Dipl. Geoecologist Andreas Ch. Braun

My background

Andreas Ch. Braun – Diploma Geoecologist

Works at the Institute of Photogrammetry and Remote SensingKernel-based (Vegetation) Classification

Support Vector Machines

Import Vector Machines

Relevance Vector Machines

Feature Extraction Methods & Data Mining

Received a special Ph. D. scholarship in 2010 from the german „Initiative for Excellence“

For a case study on Deforestation and Forest Degradation in Chile


The project on Deforestation in Chile

Analyse impact of substitution of native forests with plantations (Pinus, Eucalyptus, Populus)

Landscape fragmentation

Habitat loss

Biodiversity loss

Approach:

Biodiversity data (point data) in the field, interpolate via remote sensing/geoinformation on entire area (areal data)


How can we get from here....

Overall Accuracy 61,3%


.... to here?

Overall Accuracy 80,8% (+19,5)


Review: Image Morphology

Im. Matrix B Structuring Element S Im.Matrix B

Erosion: B⊖S :={z | Sz ⊆ B} → All Pixels in S must be in foreground

Dilatation: B⊕S :={z | Sz ∩ B ≠ ∅} → Min. 1 Pixel in S must be in foreground

Opening: Erosion dann DilatationClosing: Dilatation dann Erosion

Original Erosion Dilatation Opening Closing


How can mathematical morphology help?

Pinus radiata plantation

Populus nigra plantationNothofagus spec.forest



Toy-Example: Classification of plantations, forests, open soils




Original Opening Closing



By using math. morphology, pixels are getting „more intelligent“. They „know“ something about their neighbour pixels.

Math. Morphology is one possibility of integrating the spatial context into a spectral classification.

„Mathematical morphology is a theory aiming to analyse the spatial relationships between pixels“ (Fauvel et al., 2008, p.3805)


Morphological Attribute Profiles

M. Dalla Mura, J. A. Benediktsson, B. Waske, L. Bruzzone (2010): „Morphological Attribute Profiles for the Analysis of Very High Resolution Images“. - IEEE Transactions on Geoscience and Remote Sensing, Vol. 48(10).

Enhancements to the research on morphology in image classification by J.A.Benediktsson.

Multilevel image analysis through opening, closing following these criteria:Area

Moment of inertia

Std. Deviation

Diag. Of Bounding Box

Not only one filter size but a vast range of different structuring elements.

Graph-based approach increases computational performance.


Graph-based approach

Math. Morphology so far on binary images. How can grayscale images be used?

Grayscale image is a stack of binary thresholds (e.g.. 8bit, [0,...,255])

Within this stack, a 256 level graph of connected components exits.

Intensity IKA

IKA

> 80 IKA

> 120 IKA

> 200 IKA

> 240


Morphological profile

For these connected components (CC), certain criteria are checkedArea: Is the area of a CC < the area of the structuring element ?

Inertia: Is the extendedness of a CC < structuring element ?

Std. σ: ...

Diag. BB: ...

If criteria are met, one image opening and one image closing is performed.

Not only one structuring element is used, but an entire range → morphological profile.


Morphological profile

Afterwards, for classification we have:One original image Im

Openings Opn, n=1,...,i, for different structuring elements

Closings Cln,

n=1,...,i, for different structuring elements

The morphological profile (MP) (Pesaresi, Benediktsson, 2000) is then:MP={Cl

n, ...Im,...Op

n}

Instead of using only one channel and one MP, we can compute this on many channels, resulting in many Mps: extended morphological profile (EMP) (Benediktsson et al., 2005, Fauvel et al., 2008)

EMP={MPk1

, MPk2

, … , MPkm

}

Im Op1

Op2

Op3

Cl1

Cl2

Cl3


Additional features for classification

For each channel of Landsat ETM+, we compute the featuresArea: 2 per λ (Opening, Closing)

Inertia: 2 per λ

Std.: 2 per λ

Diag.BB: 2 per λ

For 8 different λ

8(features) * 8(channels) * 8(lambdas) = 512 new features for classification


Classification of Landsat ETM+ image

3 Subsets

1: Forested area

2: Urban area

3: Agricultural area


Subset 1: Forested area



Subset 2: Urban area



Subset 3: Agricultural area



Conclusions

Morphological Attribute Profiles are a very good, though implicit, method of integrating spatial context into spectrally motivated classification.

Especially recommendable for classification of textured classed.

Accuracy on three subsets in a image of Chile could be raised significantly.


Challenges

High dimensional feature space (>>500 features) can not be processed with standard methods (maximum likelihood).

Specialized methods needed: kernel based:Support vector machines

Import vector machines

Relevance vector machines

Considerable programming effort.

Computational expense requires high-perfomance PC (8-core processor with >120 GB Ram in our case)


References

M. Dalla Mura, J. A. Benediktsson, B. Waske, L. Bruzzone (2010): „Morphological Attribute Profiles for the Analysis of Very High Resolution Images“. - IEEE Transactions on Geoscience and Remote Sensing, Vol. 48(10).

M. Fauvel, J.A. Benediktsson, J. Chanussot, J.R. Sveinsson (2008): „Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles“. - IEEE Transactions on Geoscience and Remote Sensing, Vol. 46(10).

J.A. Benediktsson, J.A. Palmason, J.R. Sveinsson (2005): „Classification of Hyperspectral Data From Urban Areas Based on Extended Morphological Profiles“. - IEEE Transactions on Geoscience and Remote Sensing, Vol. 46(10).

P. Soille, M. Pesaresi (2002): „Advances in mathematical morphology applied to geoscience and remote sensing“. - IEEE Transactions on Geoscience and Remote Sensing, Vol. 40(9).

M. Pesaresi, J.A. Benediktsson (2000): „Image Segmentation based on the derivate of the morphological profile“.- In: Mathematical Morphology and Its Application to Image and Signal Processing, J. Goustsias, L. Vincent, D.S. Bloomberg, Eds. Norwell, MA: Kluwer, 2000.

developing a classification framework for landcover landuse change analysis in chile

Education

geoecologist andreas

remote sensing2

remote sensing4

remote sensing9

remote sensing10

remote sensinggeoinformation

remote sensing

remote sensing3