Developing a classification framework for landcover landuse change analysis in Chile
Transcript of 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
Institute of Photogrammetry and Remote Sensing3 22.07.11 Dipl. Geoecologist Andreas Ch. Braun
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)
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How can we get from here....
Overall Accuracy 61,3%
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.... to here?
Overall Accuracy 80,8% (+19,5)
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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
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How can mathematical morphology help?
Pinus radiata plantation
Populus nigra plantationNothofagus spec.forest
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How can mathematical morphology help?
Toy-Example: Classification of plantations, forests, open soils
Institute of Photogrammetry and Remote Sensing9 22.07.11 Dipl. Geoecologist Andreas Ch. Braun
How can mathematical morphology help?
Toy-Example: Classification of plantations, forests, open soils
Institute of Photogrammetry and Remote Sensing10 22.07.11 Dipl. Geoecologist Andreas Ch. Braun
How can mathematical morphology help?
Toy-Example: Classification of plantations, forests, open soils
Original Opening Closing
Institute of Photogrammetry and Remote Sensing11 22.07.11 Dipl. Geoecologist Andreas Ch. Braun
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)
Institute of Photogrammetry and Remote Sensing12 22.07.11 Dipl. Geoecologist Andreas Ch. Braun
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.
Institute of Photogrammetry and Remote Sensing13 22.07.11 Dipl. Geoecologist Andreas Ch. Braun
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
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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.
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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
Institute of Photogrammetry and Remote Sensing16 22.07.11 Dipl. Geoecologist Andreas Ch. Braun
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
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Classification of Landsat ETM+ image
3 Subsets
1: Forested area
2: Urban area
3: Agricultural area
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Subset 1: Forested area
Overall Accuracy 61,3%
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Subset 1: Forested area
Overall Accuracy 80,8% (+19,5)
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Subset 2: Urban area
Overall Accuracy 75,5%
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Subset 2: Urban area
Overall Accuracy 92,2% (+16,7)
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Subset 3: Agricultural area
Overall Accuracy 62,2%
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Subset 3: Agricultural area
Overall Accuracy 89,2% (+27,7)
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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.
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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)
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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.