The document discusses adapting the IR-MAD change detection method for use with hyperspectral data. It proposes using principal component analysis (PCA) for feature reduction before applying IR-MAD to address the high dimensionality of hyperspectral data. An initial change mask is also used to eliminate strong changes and isolate no-change pixels for analysis. Experiments on Landsat and hyperspectral data demonstrate the effectiveness of the approaches. Current work involves using Markov random fields to incorporate spatial information and generate a final change classification map.

1. Change detection in Hyperspectral data Dr. Prashanth Marpu (marpu@hi.is) Prof. Jon Atli Benediktsson University of Iceland, Reykjavik, Iceland Prof. Paolo Gamba University of Pavia, Pavia, Italy

4. Our approach Ongoing Work FR Time 1 Time 2 Initial change mask Change detection using IR-MAD method Post-processing Change map

10. Multispectral change detection using IR-MAD and initial change mask.

11. Experiments with ICM Landsat ETM+ images over Juelich, Germany taken in May, June and August, 2001. Major changes due to agricultural regions. Only the human settlement areas and mining area remain unchanged.

12. Experiments May- June May- August

13. Experiments Without Mask With Mask May -June

14. Experiments

15. Experiments Without Mask With Mask Automatic radiometric normalization

16. Experiments May- August Without Mask With Mask

17. Experiments Without Mask With Mask Automatic radiometric normalization

19. Effect of feature reduction in the multispectral case Comparison of the slopes for radiometric correction using manually selected no-change pixels and automatically selected no-change pixels using different inputs.

21. AISA Eagle data from Hungary (2007, 2008) 252 bands ICM using difference vector Chi-square image resulting from 8 PCs

22. CHRIS-PROBA images from Tor Vergata, Rome February , 2006 18 bands October, 2006 Chi- Square image from 6 PCs

25. Chi-sqr image Change/No-change labels Change Classification