approach hyperspectral

1. A New Subspace Approach
for Supervised Hyperspectral
Image Classification
Jun Li1,2, José M. Bioucas-Dias2 and Antonio Plaza1
1Hyperspectral Computing Laboratory
University of Extremadura, Cáceres, Spain
2Instituto de Telecomunicaçoes, Instituto Superior Técnico, TULisbon, Portugal
Contact e-mails: {junli, aplaza}@unex.es, bioucas@lx.it.pt

2. Talk Outline:
1. Challenges in hyperspectral image classification
2. Subspace projection
2.1. Subspace projection-based framework
2.2. Considered subspace projection techniques: PCA versus HySime
2.3. Integration with different classifiers (LDA, SVM, MLR)
3. Experimental results
3.1. Experiments with AVIRIS Indian Pines hyperspectral data
3.2. Experiments with ROSIS Pavia University hyperspectral
4. Conclusions and future research lines
A New Subspace Approach for Hyperspectral Classification
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

3. Concept of hyperspectral imaging using NASA Jet Propulsion Laboratory’s Airborne Visible Infra-Red Imaging Spectrometer
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Challenges in Hyperspectral Image Classification

4. Panchromatic
Hyperspectral
(100’s of bands)
Multispectral
(10’s of bands)
Challenges in Hyperspectral Image Classification
Ultraspectral
(1000’s of bands)
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Challenges in hyperspectral image classification
• Imbalance between dimensionality and training samples, presence of mixed pixels

5. Challenges in hyperspectral image classification
• The special characteristics of hyperspectral data pose several processing problems:
1. The high-dimensional nature of hyperspectral data introduces important
limitations in supervised classifiers, such as the limited availability of
training samples or the inherently complex structure of the data
2. There is a need to address the presence of mixed pixels resulting from
insufficient spatial resolution and other phenomena in order to properly
model the hyperspectral data
3. There is a need to develop computationally efficient algorithms, able to
provide a response in a reasonable time and thus address the computational
requirements of time-critical remote sensing applications
• In this work, we evaluate the impact of using subspace projection techniques prior to
supervised classification of hyperspectral image data while analyzing each of the
aforementioned items
Challenges in Hyperspectral Image Classification
3
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

6. Talk Outline:
1. Challenges in hyperspectral image classification
2. Subspace projection
2.1. Subspace projection-based framework
2.2. Considered subspace projection techniques: PCA versus HySime
2.3. Integration with different classifiers (LDA, SVM, MLR)
3. Experimental results
3.1. Experiments with AVIRIS Indian Pines hyperspectral data
3.2. Experiments with ROSIS Pavia University hyperspectral
4. Conclusions and future research lines
A New Subspace Approach for Hyperspectral Classification
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

7. Subspace Projection-Based Framework
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Subspace projection-based framework.-
• Hyperspectral image data generally lives in a lower-dimensional subspace compared
with the input feature dimensionality
• This can be exploited to address ill-posed problems given by limited training samples
• The projection into such subspaces allows us to specifically avoid spectral confusion
due to mixed pixels, thus reducing their impact in the subsequent classification process
J. Li, J. M. Bioucas-Dias and A. Plaza, “Spectral-spatial hyperspectral image segmentation using sub-
space multinomial logistic regression and Markov random fields,” IEEE Transactions on Geoscience and
Remote Sensing, in press, 2011.

8. Component 1
Component 2
Considered Subspace Projection Techniques: PCA versus HySime
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Principal Component Analysis (PCA).-
• High-dimensional data can be transformed effectively according to its distribution in feature
space (e.g. by finding the most important directions or axes, establishing those axes as the
references of a new coordinate system which takes into account data distribution)
• Orders the resulting components in decreasing order of variance

9. 6
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Principal Component Analysis (PCA).-
• High-dimensional data can be transformed effectively according to its distribution in feature
space (e.g. by finding the most important directions or axes, establishing those axes as the
references of a new coordinate system which takes into account data distribution)
• Orders the resulting components in decreasing order of variance
Band PCA 1 Band PCA 2 Band PCA 3 Band PCA 4 Band PCA 5
Band PCA 6 Band PCA 7 Band PCA 8 Band PCA 9 Band PCA 10
Band PCA 11 Band PCA 12 Band PCA 13 Band PCA 14 Band PCA 15
Band PCA 16 Band PCA 17 Band PCA 18 Band PCA 19 Band PCA 20
Considered Subspace Projection Techniques: PCA versus HySime

10. 7
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Hyperspectral Signal Identification by Minimum Error (HySime).-
• A recently developed method for subspace identification in remotely sensed hyperspectral
data, which offers several additional features with regards to principal component analysis and
other subspace projection techniques
J. M. Bioucas-Dias and J. M. P Nascimento, “Hyperspectral subspace identification,” IEEE
Transactions on Geoscience and Remote Sensing, vol. 46, no. 8, pp. 2435-2445, 2008.
Principal Component Analysis
• Seeks for the projection that best
represents the original hyperspectral
data in least square sense
• Reduces the original signal into
subset of eigenvectors without
computing any noise statistics
• The difficulty in getting reliable
noise estimates from the resulting
eigenvalues is that these eigenvalues
still represent mixtures of signal
sources and noise
HySime
• HySime finds the subset of
eigenvectors and the correspondent
eigenvalues by minimizing the mean
square error between the original
signal and its projection onto the
eigenvector subspace
• Uses multiple regressions for the
estimation of the noise and signal
covariance matrices
• Optimally represents the original
signal with minimum error
Considered Subspace Projection Techniques: PCA versus HySime

11. Supervised Classification Framework Tested in this Work
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Test classification accuracy
PCA, HySime
Supervised Classification Framework.-
• Includes subspace projection and supervised classification based on training samples:
Subspace projection
Supervised classifier
Training
Samples
Test
Samples
Randomly selected

12. 9
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Integration of subspace-based framework with different classifiers.-
• Three different supervised classifiers tested in this work:
1. Linear discriminant analysis (LDA): find a linear combination
of features which separate two or more classes; the resulting
combination may be used as a linear classifier (only linearly
separable classes will remain separable after applying LDA)
2. Support vector machine (SVM): constructs a set of
hyperplanes in high-dimensional space; a good separation is
achieved by the hyperplane that has the largest distance to the
nearest training data points of any class
3. Multinomial logistic regression (MLR): models the posterior
class distributions in a Bayesian framework, thus supplying (in
addition to the boundaries between the classes) a degree of
plausibility for such classes
Integration with different classifiers (LDA, SVM, MLR)

13. Talk Outline:
1. Challenges in hyperspectral image classification
2. Subspace projection
2.1. Classic techniques for subspace projection: PCA versus HySime
2.2. Subspace projection-based framework
2.3. Integration with different classifiers (LDA, SVM, MLR)
3. Experimental results
3.1. Experiments with AVIRIS Indian Pines hyperspectral data
3.2. Experiments with ROSIS Pavia University hyperspectral
4. Conclusions and future research lines
A New Subspace Approach for Hyperspectral Classification
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

14. AVIRIS Indian Pines data set.-
• Challenging classification scenario due to spectrally similar classes
• Early growth stage of the agricultural features (only around 5% coverage of soil)
• 145x145 pixels, 202 spectral bands, 16 ground-truth classes
• 10366 labeled pixels (random training subsets evenly distributed among classes)
Experimental Results Using Real Hyperspectral Data Sets
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
False color composition Ground-truth

15. AVIRIS Indian Pines data set.-
• Classification results using 160 training samples (10 training samples per class)
• For the SVM classifier we used the Gaussian RBF kernel after testing other kernels
• The mean accuracies (after 10 Monte Carlo runs) and processing times are reported
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Experimental Results Using Real Hyperspectral Data Sets

16. AVIRIS Indian Pines data set.-
• Classification results using 240 training samples (15 training samples per class)
• For the SVM classifier we used the Gaussian RBF kernel after testing other kernels
• The mean accuracies (after 10 Monte Carlo runs) and processing times are reported
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Experimental Results Using Real Hyperspectral Data Sets

17. AVIRIS Indian Pines data set.-
• Classification results using 320 training samples (20 training samples per class)
• For the SVM classifier we used the Gaussian RBF kernel after testing other kernels
• The mean accuracies (after 10 Monte Carlo runs) and processing times are reported
13
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Experimental Results Using Real Hyperspectral Data Sets

18. 14
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
AVIRIS Indian Pines data set.-
• Classification results using 320 training samples (20 training samples per class)
SVM (OA=65.36%) Subspace SVM (OA=70.33%) LDA (OA=50.74%) Subspace LDA (OA=54.90%)
Linear MLR (OA=60.38%) Subspace MLR (OA=67.53%) Ground-truth
Experimental Results Using Real Hyperspectral Data Sets

19. ROSIS Pavia University data set.-
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Experimental Results Using Real Hyperspectral Data Sets
False color composition Ground-truth Training data
Overall classification accuracies and kappa coefficient (in the parentheses) using different training sets for the ROSIS Pavia
University

20. Conclusions and Future Lines.-
• We have evaluated the impact of subspace projection on supervised classification
of remotely sensed hyperspectral image data sets
• Two dimensionality reduction methods have been used: PCA and HySime,
although many others are available and could be used: MNF, OSP, VD
• Three different supervised classifiers considered: LDA, SVM, MLR
• Experimental results indicate that different approaches for hyperspectral image
classification approaches can benefit from subspace projection, particularly
when very limited training samples are available
• Subspace projection can be naturally integrated with multinomial logistic
regression (MLR) classifiers, which greatly benefit from dimensionality reduction
• Future work will focus on the evaluation of other subspace projection approaches
and hyperspectral data sets
Conclusions and Hints at Plausible Future Research
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

21. •IEEE J-STARS Special Issue on Hyperspectral Image and Signal Processing
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

22. A New Subspace Approach
for Supervised Hyperspectral
Image Classification
Jun Li1,2, José M. Bioucas-Dias2 and Antonio Plaza1
1Hyperspectral Computing Laboratory
University of Extremadura, Cáceres, Spain
2Instituto de Telecomunicaçoes, Instituto Superior Técnico, TULisbon, Portugal
Contact e-mails: {junli, aplaza}@unex.es, bioucas@lx.it.pt