1. Keng-Hao Liu and Chein-I Chang Remote Sensing Signal and Image Processing Laboratory (RSSIPL) Department of Computer Science and Electrical Engineering University of Maryland, Baltimore County (UMBC) Baltimore, MD 21250 Dynamic Band Selection For Hyperspectral Imagery keng3@umbc.edu
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12. Hyperspectral Images Used for Experiments (1) HYDICE Data: 64x64 169 bands hyperspectral image with spatial resolution is 20m. Ground truth (desired signatures) Image scene p 11 , p 12 , p 13 p 211 , p 22 , p 23 p 221 p 311 , p 312 , p 32 , p 33 p 411 , p 412 , p 42 , p 43 p 511 , p 52 , p 53 p 521 interferer grass tree road undesired signatures Spectral of five panels Classifier: FCLS Band De-correlation (BD) is applied after BP with σ= 0.1
13. HYDICE Data Experiments Shannon BDA results of HYDICE Data signature m j n VD π j q j n j (BDA) Shannon coding m 1 =p 1 (panels in row1) 9 SID 0.0172 15 SAM 0.0462 14 m 2 =p 2 (panels in row2) 9 SID 0.0295 15 SAM 0.0779 13 m 3 =p 3 (panels in row3) 9 SID 0.0358 14 SAM 0.0897 13 m 4 =p 4 (panels in row4) 9 SID 0.0695 13 SAM 0.1004 13 m 5 =p 5 (panels in row5) 9 SID 0.1070 13 SAM 0.1140 13 m 6 (grass) 9 SID 0.1007 13 SAM 0.1396 12 m 7 (road) 9 SID 0.0565 14 SAM 0.1035 13 m 8 (tree) 9 SID 0.2869 12 SAM 0.1479 12 m 9 (interferer) 9 SID 0.2969 11 SAM 0.1808 12
15. HYDICE Data Experiments ROC performance of 5 row panels using FCLS Panels in row 1 Panels in row 2 Panels in row 3 Panels in row 4 Panels in row 5 BDA VD Y axis: Area under curve of ROC ( P D versus P F ) X axis: Number of selected bands, p
16. HYDICE Data Experiments Average ROC performance of 5 row panels Average performance of 5 row panels BDA range VD 2VD
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18. Hyperspectral Images Used for Experiments (2) Class map AVIRIS (Purdue) Data: 145x145 202 bands hyperspectral image. Image scene class1 class2 class3 class4 class5 class6 class7 class8 class9 class10 class11 class12 class13 class14 class15 class16 17 Classes maps Data samples are heavily-mixed Classifier: MLC Band De-correlation (BD) is applied after BP with σ= 0.1
19. Purdue Data Experiments BDA results of Purdue Data signature m j n VD π j q j j n j (BDA) Shannon coding m 1 (class 1) 29 SID 0.0128 36 m 2 (class 2) 29 SID 0.0437 34 m 3 (class 3) 29 SID 0.0392 34 m 4 (class 4) 29 SID 0.0140 36 m 5 (class 5) 29 SID 0.1341 32 m 6 (class 6) 29 SID 0.0316 34 m 7 (class 7) 29 SID 0.0042 37 m 8 (class 8) 29 SID 0.0104 36 m 9 (class 9) 29 SID 0.0154 36 m 10 (class 10) 29 SID 0.0543 34 m 11 (class 11) 29 SID 0.0480 34 m 12 (class 12) 29 SID 0.0430 34 m 13 (class 13) 29 SID 0.0277 35 m 14 (class 14) 29 SID 0.2293 32 m 15 (class 15) 29 SID 0.0580 34 m 16 (class 16) 29 SID 0.2105 32 m 17 (BKG) 29 SID 0.0239 35
20. Purdue Data Experiment MLC classification results of 16 classes class1 class2 class3 class4 class5 class6 class7 class8 class9 class10 BDA VD Y axis: MLC classification rate in percent% X axis: Number of selected bands, p Classes 1 to 10
21. Purdue Data Experiment Average performance of 16 classes MLC classification results class11 class12 class13 class14 class15 class16 VD 2VD BDA range Classes 11 to 16
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25. Thank You ! keng3@umbc.edu http://www.umbc.edu/rssipl/
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