1. Particle Swarm Optimization-based Dimensionality Reduction for Hyperspectral Image Classification He Yang, Jenny Q. Du Department of Electrical and Computer Engineering Mississippi State University, MS 39762, USA

10. ● PSO is used to search the solution of . ● The initial particles are spread sparsely in the whole problem space in iteration 1. ● The particles start to be pulled by the update procedure to the optimal regions from iteration 25 to iteration 75. ● All the particles are gathered at the optimum point by the updating procedure. Iteration 1 Iteration 25 Iteration 50 Iteration 75

12. PSO for Band Selection  Algorithm: 1. Assume p bands are to be selected. Randomly initialize M particles x id , and each particle includes p indices of the bands to be selected. 2. Evaluate the objective function for each particle, and determine the local and global optimal solution p id and p gd respectively. 3. Update all the particles. 4. If the algorithm is converged, then stop; otherwise, go to step 2. 5. The particle yielding the global optimum solution p gd is the final result.  MEAC:  JM distance:  Objective function:

13. Illustration of PSO-based band selection (Selecting 6 bands from L bands)

14. Convergence curves of PSO-based band selection (MEAC)

15. Convergence curve of PSO-based band selection (JM distance)

16. Decision Fusion Hyperspectral Image Data Supervised classifier (SVM) Use unsupervised result to segment supervised result Unsupervised classifier (Kmeans, Mean-Shift) (Weighted) Majority Voting Final Decision ● H. Yang, Q. Du, and B. Ma, “Decision fusion on supervised and unsupervised classifiers for hyperspectral imagery,” IEEE Geoscience and Remote Sensing Letters , vol. 7, no. 4, pp. 875-879, Oct. 2010.

19. HYDICE Experiment Training Test Road 55 892 Grass 57 910 Shadow 50 567 Trail 46 624 Tree 49 656 Roof 52 1123

23. Classification accuracy from different methods in HYDICE experiment (with 6 bands or 6 PCs) Road Grass Shadow Trail Tree Roof OA AA Kappa svm(pca) 99.0 98.6 82.0 92.3 98.8 84.8 92.6 92.6 91.1 svm(pso) 98.1 98.9 94.7 92.5 99.4 95.4 96.6 96.5 95.9 svm(pca)+ms 100.0 99.0 81.3 94.9 98.9 89.3 94.3 93.9 93.0 svm(pso)+ms 90.7 99.0 100.0 100.0 98.9 98.9 97.5 97.9 97.0 svm(pca)+kmeans 99.9 96.9 75.7 96.6 98.8 95.3 94.8 93.9 93.6 svm(pso)+kmeans 94.8 99.2 98.9 99.7 95.9 99.3 97.9 98.0 97.5

25. HyMap Experiment Training Test Road 73 1231 Grass 72 1072 Shadow 49 215 Soil 69 380 Tree 67 1321 Roof 74 1244

29. Classification accuracy from different methods in HyMap experiment (with 6 bands or 6 PCs) Road Grass Shadow Soil Tree Roof OA AA Kappa svm(pca) 92.4 98.9 97.2 90.8 96.4 81.4 92.2 92.8 90.3 svm(pso) 94.9 98.3 98.1 85.2 93.9 89.6 93.6 93.3 91.9 svm(pca)+ms 96.3 100.0 98.1 100.0 97.7 85.5 95.2 96.3 94.0 svm(pso)+ms 97.3 96.0 100.0 98.7 98.9 100.0 98.2 98.5 97.7 svm(pca)+kmeans 99.2 99.7 86.9 71.7 98.7 81.8 92.9 89.7 91.0 svm(pso)+kmeans 96.1 96.6 95.9 98.1 99.5 98.2 97.6 97.4 96.9