Laser & Optoelectronics Progress, Volume. 56, Issue 21, 212802(2019)
Hyperspectral Image Classification Based on Clustering Dimensionality Reduction and Visual Attention Mechanism
Figures & Tables(6)
Fig. 1. Indian hyperspectral image dataset. (a) False-color composite imge; (b) labeled data; (c) legends of classification
Fig. 2. Pavia hyperspectral image dataset. (a) False-color composite imge; (b) labeled data; (c) legends of classificatin
Fig. 3. Band mutual information matrix of hyperspectral datasets. (a) Indian dataset; (b) Pavia dataset
Fig. 4. Results of saliency mapping of single band. (a) Band 122 and its saliency mapping selected by Indian dataset; (b) band 1 and its saliency mapping removed by Indian dataset; (c) band 94 and its saliency mapping selected by Pavia dataset; (d) band 1 and its saliency mapping removed by Pavia dataset
Table 1. Evaluation indices of classification accuracy of different methods on Indian dataset (best results are highlighted in bold)
View tableTable 1. Evaluation indices of classification accuracy of different methods on Indian dataset (best results are highlighted in bold)
Class Class name SVM PCA EAP HSD5-SVM HSD10-SVM 1 Alfalfa 0.8261 0.9706 0.9565 1.0000 0.9130 2 Corn-notill 0.6610 0.6808 0.7518 0.8504 0.8578 3 Corn-mintill 0.5584 0.6650 0.8563 0.9416 0.8718 4 Corn 0.3333 0.7200 0.9179 0.7156 0.8711 5 Grass-pasture 0.8170 0.8344 0.9316 0.9041 0.8388 6 Grass-trees 0.9408 0.7047 0.9614 0.8427 0.8990 7 Grass-pasture-mowed 0.8571 0.8750 0.9286 0.7857 0.9286 8 Hay-windrowed 0.8348 0.9914 0.9978 0.9604 1.0000 9 Oats 1.0000 1.0000 1.0000 1.0000 1.0000 10 Soybean-notill 0.6999 0.7917 0.7749 0.8657 0.8342 11 Soybean-mintill 0.7916 0.6025 0.7328 0.9357 0.9400 12 Soybean-clean 0.4742 0.7780 0.9343 0.8881 0.7336 13 Wheat 0.8872 0.9845 0.9771 0.9128 0.9436 14 Woods 0.9334 0.8771 0.9692 0.9992 0.9992 15 Buildings-grass-trees-drives 0.3569 0.8930 0.7640 0.9292 0.9373 16 Stone-steel-towers 0.9348 0.9753 0.9683 0.9565 1.0000 Kappa 0.6944 0.7126 0.8189 0.8970 0.8878 OA 0.7343 0.7430 0.8405 0.9099 0.9018 AA 0.7442 0.8340 0.9014 0.9055 0.9105 Time/s 16.70 3.29 4.21 5.70 6.99
Table 2. Evaluation indices of classification accuracy of different methods on Pavia dataset (best results are highlighted in bold)
View tableTable 2. Evaluation indices of classification accuracy of different methods on Pavia dataset (best results are highlighted in bold)
Class Class name SVM PCA EAP HSD5-SVM HSD10-SVM 1 Asphalt 0.9319 0.9541 0.9718 0.9783 0.9850 2 Meadows 0.9795 0.9583 0.9810 0.9938 0.9950 3 Gravel 0.7708 0.6850 0.9579 0.9786 0.9852 4 Trees 0.9334 0.8324 0.9043 0.8627 0.8597 5 Metal sheets 0.9915 0.9948 0.9875 0.9958 0.9815 6 Bare soil 0.8787 0.7153 0.9490 0.9919 0.9922 7 Bitumen 0.8492 0.5803 0.9955 0.9990 0.9888 8 Bricks 0.8892 0.7816 0.9842 0.9848 0.9932 9 Shadows 0.9987 0.9989 0.9991 0.9783 0.9560 Kappa 0.9144 0.8260 0.9609 0.9736 0.9752 OA 0.9357 0.8680 0.9705 0.9801 0.9813 AA 0.9136 0.8337 0.9700 0.9737 0.9707 Time/s 84.32 16.23 18.24 9.77 12.25
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Chaoping Zeng, Lijun Ju, Jianchen Zhang. Hyperspectral Image Classification Based on Clustering Dimensionality Reduction and Visual Attention Mechanism[J]. Laser & Optoelectronics Progress, 2019, 56(21): 212802
Paper Information
Category: Remote Sensing and Sensors
Received: Jun. 19, 2019
Accepted: Aug. 5, 2019
Published Online: Nov. 2, 2019
The Author Email: Jianchen Zhang (jczhang@vip.henu.edu.cn)
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