1. A New Subspace Approach for Supervised Hyperspectral Image Classification Jun Li 1,2 , José M. Bioucas-Dias 2 and Antonio Plaza 1 1 Hyperspectral Computing Laboratory University of Extremadura, Cáceres, Spain 2 Instituto 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 1 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011 Challenges in Hyperspectral Image Classification
4.
5.
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.
8.
9.
10.
11.
12.
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.
15.
16.
17.
18.
19. ROSIS Pavia University data set.- 15 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011 Experimental Results Using Real Hyperspectral Data Sets Overall classification accuracies and kappa coefficient (in the parentheses) using different training sets for the ROSIS Pavia University
20.
21.
22. A New Subspace Approach for Supervised Hyperspectral Image Classification Jun Li 1,2 , José M. Bioucas-Dias 2 and Antonio Plaza 1 1 Hyperspectral Computing Laboratory University of Extremadura, Cáceres, Spain 2 Instituto de Telecomunicaçoes, Instituto Superior Técnico, TULisbon, Portugal Contact e-mails: {junli, aplaza}@unex.es, bioucas@lx.it.pt
