This document summarizes research on classifying hyperspectral data using extended morphological attribute profiles (MAPs) based on different feature extraction techniques. MAPs built using standard deviation attributes and techniques like PCA, KPCA, DAFE and DBFE were used to classify data from the University of Pavia and Indian Pines sites. KPCA and DAFE generally produced the best results, with KPCA MAPs achieving overall accuracies over 90% on both datasets when used with random forests or SVMs. Further optimization of feature extraction and threshold selection for MAP construction is ongoing.

1. Classification Using Extended Morphological Attribute Profiles Based On Different Feature Extraction Techniques Stiijn Peeters University of Antwerp, Antwerp, Belgium Dr. Prashanth Marpu Mattia Pedergnana Prof. Jon Atli Benediktsson University of Iceland, Reykjavik, Iceland Dr. Mauro Dalla Mura Prof. Lorenzo Bruzzone University of Trento, Trento, Italy

5. Morphological Profile (MP) and Morphological Attribute Profile (MAP)

6. Morphological Profiles When dealing with real images it is difficult to identify a single filter parameter suitable to handle all the objects in the image. Perform a multilevel analysis by using several values for the filter parameters. Build a stack of images with different degrees of filtering. Morphological Profile (MP) M. Pesaresi and J. A. Benediktsson, “A new approach for the morphological segmentation of high-resolution satellite imagery," IEEE Transactions on Geoscience and Remote Sensing , vol. 39, no. 2, pp. 309-320, 2001.

7. Morphological Profiles Closing Profile Opening Profile Square SE Sizes: 7, 13, 19, 25 Morphological Profiles (MPs) are composed by a sequence of opening and closing with SE of increasing size. MP

8. X 1 X 1 X 1 X 1 MP X 1 Hyperspectral Image X MP MP X 1 Morphological profile 1 Morphological profile n Feature Reduction F 1 F 2 F n X 1 X 1 Extended Morphological Profile X 1 X 1 X 1 X 1 Extended Morphological Profile

9. Attribute Profiles Thickening Profile Thinning Profile Square SE (MP) Sizes: 7, 13, 19 Area Attribute λ : 45, 169, 361 Criterion : Area > λ Moment of Inertia Attribute λ : 0.2, 0.1, 0.3 Criterion : Inertia > λ STD Attribute λ : 10, 20, 30 Criterion : STD > λ

10. Selection of thresholds for constructing MAP Master Thesis: Mattia Pedergnana (University of Iceland, Iceland and University of Trento, Italy) Optimal Automatic Construction of Morphological Profiles In this study, we only use the attribute profile generated using the standard deviation attribute. The thresholds to build the profile are estimated for every feature separately from the range of standard deviation values of the training samples of all the classes. So, different threshold values are used for diferent profiles. A more general approach to use a big range of attributes has been recently proposed. An entire profile using a wide range of attributes and wide range of thresholds is built and a newly proposed hybrid genetic algorithm is used for feature selection.

11. Results

12. Data used ROSIS University of Pavia

13. Data used AVIRIS Indian Pine

15. Results: University of Pavia data PCA SVM OA 92.01% AA 92.17% k 0.8957 Random Forest OA 91.31% AA 87.96% k 0.8894

16. Results: University of Pavia data Kernel PCA RF OA 92.2% AA 95.02% k 0.8993 SVM OA 92.31% AA 93.96% k 0.9002

17. Results: University of Pavia data DAFE RF OA 96.25% AA 96.28% k 0.951 SVM OA 92.69% AA 93.27% k 0.9119

18. Results: University of Pavia data DBFE RF OA 95.09% AA 95.32% k 0.9386 SVM OA 93.45% AA 94.16% k 0.9145

19. Summary of Results University of Pavia Extended Attribute Profile using Standard deviation RF SVM PCA KPCA DAFE DBFE PCA KPCA DAFE DBFE OA% 91.31 92.2 96.28 95.32 92.01 92.31 93.27 93.45 AA% 87.96 95.02 96.25 95.09 92.17 93.96 92.69 94.16 k 0.886 0.899 0.951 0.939 0.896 0.90 0.912 0.914

20. Extended Morphological Profiles Params: Initial Size: 1 Step: 2 Number Of Opening/Closing: 3 PCA RF DAFE RF DBFE RF KPCA RF PCA SVM DAFE SVM DBFE SVM KPCA SVM AA (%) 90.89 95.12 96.75 93.87 91.37 92.32 94.92 94.74 OA (%) 84.75 94.88 96.107 87.89 86.92 88.93 90.00 91.47 k 0.8048 0.9324 0.9487 0.844 0.8326 0.8579 0.8720 0.8898

21. Results: Indian Pine PCA RF OA 92.20% AA 95.32% k 0. 9100 SVM OA 88.94% AA 92.96% k 0.8738

22. Results: Indian Pine Kernel PCA RF OA 92.87% AA 96.01% k 0.9183 SVM OA 88.93 % AA 93.36 % k 0.8737

23. Results: Indian Pine DAFE RF OA 77.21 % AA 87.62 % k 0.7427 SVM OA 68.70% AA 71.31% k 0.6464

24. Results: Indian Pine DBFE RF OA 81.28% AA 87.78% k 0.7866 SVM OA 73.13% AA 79.81% k 0.6954

25. Summary of Results Indian Pine Extended Attribute Profile using Standard deviation RF SVM PCA KPCA DAFE DBFE PCA KPCA DAFE DBFE OA% 92.20 92.87 77.21 81.28 88.94 88.93 68.70 73.13 AA% 95.32 96.01 87.62 87.78 92.96 93.36 71.31 79.81 k 0.910 0.918 0.743 0.787 0.874 0.8737 0.646 0.695

26. Extended Morphological Profiles Params: Initial Size: 1 Step: 2 Number Of Opening/Closing: 3 PCA RF DAFE RF DBFE RF KPCA RF PCA SVM DAFE SVM DBFE SVM KPCA SVM AA (%) 93.54 87.67 86.69 95.66 92.90 69.84 78.93 92.702 OA (%) 87.75 79.08 78.98 92.01 87.15 66.61 71.36 86.67 k 0.8607 0.7627 0.7615 0.9085 0.8533 0.6231 0.6763 0.8483