The document presents a novel hyperspectral unmixing approach called uccm-SVM that converts the abundance quantification problem into a classification problem using support vector machines. The approach is tested on both simulated and real hyperspectral images and is shown to outperform traditional mean-based techniques like FCLS in terms of accuracy while having lower computational costs for smaller training set sizes. Future work to improve the method includes enhancing performance while reducing computation for larger training sets.

1. IGARSS 2011, Vancouver, Canada HYPERSPECTRAL UNMIXING USING A NOVEL CONVERSION MODEL Fereidoun A. Mianji, Member, IEEE, Shuang Zhou, Member, IEEE, Ye Zhang, Member, IEEE Presentation by: Shuang Zhou School of Electronics and Information Technology Harbin Institute of Technology, Harbin, China

9. 3. Structure of the Proposed Approach uccm-SVM

10. 3. Structure of the Proposed Approach uccm-SVM Fig. 3.1. The layout and process flow of uccm-SVM designed for unmixing with a resolution of 1%. Yes No SVM training SVM1: trained for 0% of “ one” SVM101: t rained for 100% of “ one” SVM2: trained for 1% of “ one” 1 - Designating sample set i as “ one” (initial ize with i=1) 2 - Making synthetic classes using remaining p - 1 training sample sets (“rest”) Quantification result for “one” in all pixels (fractional image) … p endmembers: p training sample set s including extracted pure pixel vectors Image Pixel s i> p ? Endmember fractions rescaling to unity Next i (endmember)

13. 5. Experiments with a Simulated Image Made of Real Hyperspectral Data Results Fig. 5.2 Unmixing result for roof by FCLS. Fig. 5.3. Unmixing result for roof by uccm-SVM. We expect to see only 3 peaks, with the amplitue&width of 0.1&15, 0.2&10, and 0.3&5 starting in pixel locations in 300, 750, and 1200, respectively. As can be seen, our method presents a much better result.

17. 6. Experiments with Real Hyperspectral Image with Implanted Mixed Pixels Unmixing the Mixed Pixels Implanted in ROI1 Fig. 6.3. (from left to right) True abundance of corn-notill in the border line of ROI1, unmixing results by FCLS, and unmixing result by uccm-SVM. Table 6.1. Comparison of uccm-SVM with FCLS in terms of average square error and computational time for Indian Pine ROI1. 0.39 85.5 3.16 Uccm-SVM 0.44 N/A 17.01 FCLS Test time (s) Training time (s) Average square error (%) Technique

19. 7. Experiments with Real Hyperspectral Image with Many Endmembers Results ASE: uccm-SVM performs better than FCLS on majority of single classes and in ASE average over all sizes of training sets. Computationally: uccm-SVM is faster than FCLS for low number of training samples and slower for higher numbers. Table. 7.1. Average square error (ASE) for the obtained fractional images using FCLS and uccm-SVM for unmixing of The University of Pavia data set (downsampled). 985.2 511.1 2.35 1.09 1.44 1.16 3.45 0.98 4.12 1.23 3.78 3.89 uccm-SVM 186.3 - 4.45 3.32 2.78 2.23 6.73 0.87 50.1 2.41 8.23 8.47 FCLS 100 94.3 6.9 3.35 1.22 1.89 1.84 5.00 0.82 6.07 1.61 6.60 5.15 uccm-SVM 239.8 - 4.75 4.74 2.81 2.41 6.43 0.91 5.13 3.27 8.24 8.80 FCLS 10 15.5 0.45 4.84 0.69 2.45 2.15 6.15 0.95 6.77 4.45 13.29 6.55 uccm-SVM 223.4 - 5.57 7.75 3.10 2.17 6.01 1.17 6.93 4.04 8.51 10.43 FCLS 2 Test Train Ave. 9 8 7 6 5 4 3 2 1 Time (s) Average square errors (ASE) for classes and also average ASE (%) Method # Training samples

21. Thanks For Your Attention Shuang Zhou School of Electronics and Information Technology Harbin Institute of Technology, Harbin, China