Developing a classification framework for landcover landuse change analysis in Chile
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Developing a classification framework for landcover landuse change analysis in Chile

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Developing a classification framework for landcover landuse change analysis in Chile Presentation Transcript

  • 1. Developing a classification framework for landcoverlanduse change analysis in ChileDipl. Geoecologist Andreas Ch. Braun – Karlsruhe Institute of Technology – KITInstitute of Photogrammetry and Remote Sensing - IPFKIT – University of the State of Baden-Wuerttemberg andNational Research Center of the Helmholtz Association www.kit.edu
  • 2. My background Andreas Ch. Braun – Diploma Geoecologist Works at the Institute of Photogrammetry and Remote Sensing Kernel-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 Chile2 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 3. 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)3 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 4. How can we get from here.... Overall Accuracy 61,3%4 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 5. .... to here? Overall Accuracy 80,8% (+19,5)5 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 6. 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 Dilatation Closing: Dilatation dann Erosion Original Erosion Dilatation Opening Closing6 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 7. How can mathematical morphology help? Pinus radiata plantation Populus nigra plantationNothofagus spec.forest 7 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 8. How can mathematical morphology help? Toy-Example: Classification of plantations, forests, open soils8 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 9. How can mathematical morphology help? Toy-Example: Classification of plantations, forests, open soils9 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 10. How can mathematical morphology help? Toy-Example: Classification of plantations, forests, open soils Original Opening Closing10 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 11. How can mathematical morphology help? 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)11 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 12. 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.12 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 13. 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]) Intensity IKA IKA > 80 IKA > 120 IKA > 200 IKA > 240 Within this stack, a 256 level graph of connected components exits.13 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 14. Morphological profile For these connected components (CC), certain criteria are checked Area: 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.14 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 15. 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={Cln, ...Im,...Opn} Cl3 Cl2 Cl1 Im Op1 Op2 Op3 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}15 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 16. Additional features for classification For each channel of Landsat ETM+, we compute the features Area: 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 classification16 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 17. Classification of Landsat ETM+ image 3 Subsets 1: Forested area 2: Urban area 3: Agricultural area17 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 18. Subset 1: Forested area Overall Accuracy 61,3%18 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 19. Subset 1: Forested area Overall Accuracy 80,8% (+19,5)19 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 20. Subset 2: Urban area Overall Accuracy 75,5%20 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 21. Subset 2: Urban area Overall Accuracy 92,2% (+16,7)21 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 22. Subset 3: Agricultural area Overall Accuracy 62,2%22 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 23. Subset 3: Agricultural area Overall Accuracy 89,2% (+27,7)23 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 24. 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.24 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 25. 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)25 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing
  • 26. 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, 2000.26 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing