Manifold Alignment for Multitemporal Hyperspectral Image Classification

1. Manifold Alignment for MultitemporalHyperspectral Image Classification H. Lexie Yang1, Dr. Melba M. Crawford2 School of Civil Engineering, Purdue University and Laboratory for Applications of Remote Sensing Email: {hhyang1, mcrawford2}@purdue.edu July 29, 2011 IEEE International Geoscience and Remote Sensing Symposium

2. Outline • Introduction • Research Motivation • Effective exploitation of information for multitemporal classification in nonstationary environments • Goal: Learn “representative” data manifold • Proposed Approach • Manifold alignment via given features • Manifold alignment via correspondences • Manifold alignment with spectral and spatial information • Experimental Results • Summary and Future Directions

3. Introduction N>>30 • Challenges for classification of hyperspectral data • temporally nonstationary spectra • high dimensionality 3 2 1 2001 2001 2002 2003 2004 2005 2006 N narrow spectral bands June July May May May May June

4. Research Motivation • Nonstationarities in sequence of images • Spectra of same class may evolve or drift over time • Potential approaches • Semi-supervised methods • Adaptive schemes • Exploit similar data geometries • Explore data manifolds Good initial conditions required

5. Manifold Learning for Hyperspectral Data • Characterize data geometry with manifold learning • To capture nonlinear structures • To recover intrinsic space (preserve spectral neighbors) • To reduce data dimensionality • Classification performed in low dimensional space Original space Manifold space 3rd dim Spectral bands n Spatial dimension 6 5 4 3 2 1 Spatial dimension 1st dim 2nd dim

6. Challenges: Modeling Multitemporal Data • Unfaithful joint manifold due to spectra shift • Often difficult to model the inter-image correspondences Data manifold at T2 Data manifolds at T1 and T2 Data manifold at T1

7. Proposed Approach: Exploit Local Structure Assumption: local geometric structures are similar Approach: Extract and optimally align local geometry to minimize overall differences Locality Spectral space at T2 Spectral space at T1

8. Proposed Approach: Conceptual Idea (Ham, 2005)

9. Proposed Approach: Manifold Alignment • Exploit labeled data for classification of multitemporal data sets Samples with class labels Samples with no class labels Joint manifold

10. Manifold Alignment: Introduction • and are 2 multitemporalhyperspectral images • Predict labels of using labeled • Explore local geometries using graph Laplacian and some form of prior information • Define Graph Laplacian • Twopotential forms of prior information: given features and pairwise correspondences [Ham et al. 2005]

11. Manifold Alignment via Given Features Minimize Joint Manifold Given Features

12. Manifold Alignment via Pairwise Correspondences Minimize Correspondences between and Joint Manifold

13. MA with spectral and spatial information • Combine spatial locations with spectral signatures • To improve local geometries (spectral) quality • Idea: Increase similarity measure when two samples are close together Weight matrix for graph Laplacian: where spatial location of each pixel is represented as

14. Experimental Results: Data Three Hyperion images collected in May, June and July 2001 May, June pair: Adjacent geographical area June, July pair: Targeted the same area May June July

15. L L L I1, I2 I1 I2 Experimental Results: Framework Graph Laplacian Prior information Joint manifold Given features Classification with KNN Correspondences Develop Data Manifold of Pooled Data

16. Manifold Learning for Feature Extraction • Global methods consider geodesic distance • Isometric feature mapping (ISOMAP) • Local methods consider pairwise Euclidian distance • Locally Linear Embedding (LLE): (Saul and Roweis, 2000) • Local Tangent Space Alignment (LTSA): (Zhang and Zha, 2004) • LaplacianEigenmaps (LE): (Belkin and Niyogi, 2004) (Tenenbaum, 2000)

17. MA with Given Features • Baseline: Joint manifold developed by pooled data 79.21 77.29 77.88 76.31 (May, June pair)

18. MA Results – Classification Accuracy • Evaluate results by overall accuracies

19. Results – Class Accuracy May, June pair Typical class Critical class (Island Interior) Critical class (Riparian) (Woodlands)

20. Summary and Future Directions • Multitemporal spectral changes result in failure to provide a faithful data manifold • Manifold alignment framework demonstrates potential for nonstationary environment by utilizing similar local geometries and prior information • Spatial proximity contributes to stabilization of local geometries for manifold alignment approaches • Future directions • Investigate alternative spatial and spectral integration strategy • Address issue of longer sequences of images

21. Thank you. Questions?

22. References • J. Ham, D. D. Lee, and L. K. Saul, “Semisupervised alignment of manifolds,” in International Workshop on Artificial Intelligence and Statistics, August 2005.

23. Backup Slides

24. Local Manifold Learning for Feature Extraction (s,f) • Local geometry preserved via various strategies for embedding • Popular local manifold learning methods • Locally Linear Embedding (LLE): (Saul and Roweis, 2000) • Local Tangent Space Alignment (LTSA): (Zhang and Zha, 2004) • LaplacianEigenmaps (LE): (Belkin and Niyogi, 2004) • Pairwise distance between neighbors computed using Gaussian kernel function - O(pN2) method • Embedding computed to minimize the total distance between neighbors

25. LE: Impact of Parameter Values • Parameter values for local embedding • s obtained via grid search • k, p obtained empirically BOT Class 3, 6 BOT Classes 1-9

26. Alignment Results: Typical Class • Island Interior

27. Alignment Results: Critical Class • Critical class: Riparian

28. Alignment Results: Critical Class • Critical class: Woodlands

29. MA Results – Classification Accuracy • Evaluate results by overall accuracies Labeled Class (Subset Data) Classified via Pooled Data Classified via Given Features Classified via Correspondences May, June pair

30. MA Results – Classification Accuracy Classified via Given Features (Spectral + spatial) Classified via Correspondences (Spectral + spatial) Labeled Class (Subset Data) Classified via Given Features (Spectral) Classified via Correspondences (Spectral) May, June pair