Developing a classification framework for landcover landuse change analysis in Chile

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 Sensing - IPF

KIT – University of the State of Baden-Wuerttemberg and
National Research Center of the Helmholtz Association www.kit.edu

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 Chile

2 22.07.11 Dipl. Geoecologist Andreas Ch. Braun Institute of Photogrammetry and Remote Sensing

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 Dilatation
Closing: Dilatation dann Erosion

Original Erosion Dilatation Opening Closing

How can mathematical morphology help?

Pinus radiata plantation

Populus nigra plantation
Nothofagus 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])

Intensity IKA IKA > 80 IKA > 120 IKA > 200 IKA > 240

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


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.


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}


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
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

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

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

Semelhante a Developing a classification framework for landcover landuse change analysis in Chile (20)

Último

Último (20)

Developing a classification framework for landcover landuse change analysis in Chile