Acta Photonica Sinica, Volume. 50, Issue 2, 103(2021)
Greedy Unsupervised Hyperspectral Image Band Selection Method Based on Variable Precision Rough Set
Figures & Tables(13)
Fig. 5. Classification and comparison results of 20 band IP data sets (
Fig. 8. Accuracy of classification results generated by different sample numbers
Table 1. Comparison of classification performance of Botswana dataset
View tableView in ArticleTable 1. Comparison of classification performance of Botswana dataset
Number of
bands
Proposed
method（β=0.1）
SVD ID WaLuDi WaLuMI RRS OA KA STD OA KA STD OA KA STD OA KA STD OA KA STD OA KA STD 5 86.64 0.855 0.577 86.21 0.851 0.488 73.64 0.714 0.329 79.76 0.781 0.861 86.12 0.85 0.425 88.77 0.878 0.481 10 90.73 0.9 0.268 94 0.935 0.293 91.14 0.904 0.192 90.85 0.901 0.407 90.88 0.901 0.32 92.89 0.923 0.227 15 93.31 0.928 0.275 94.64 0.942 0.329 93.2 0.926 0.341 93.15 0.926 0.276 92.19 0.915 0.336 94.67 0.929 0.284 20 94.08 0.936 0.262 95.3 0.949 0.37 94.23 0.938 0.251 94.64 0.942 0.316 93.46 0.929 0.287 95.12 0.936 0.249 25 95.51 0.951 0.322 95.83 0.955 0.247 94.63 0.942 0.249 95.39 0.95 0.281 94.32 0.938 0.355 95.78 0.943 0.226 30 96.02 0.957 0.343 96.72 0.964 0.373 94.97 0.946 0.191 96.18 0.959 0.187 95.02 0.946 0.37 96.95 0.954 0.252
Table 2. Comparison of classification performance of KSC dataset
View tableView in ArticleTable 2. Comparison of classification performance of KSC dataset
Number
of bands
Proposed
method（β=0.1）
SVD ID WaLuDi WaLuMI RRS OA KA STD OA KA STD OA KA STD OA KA STD OA KA STD OA KA STD 5 84.91 0.831 0.241 58.25 0.53 0.958 52.98 0.465 0.383 81.96 0.798 0.505 81.38 0.791 0.216 80.77 0.785 0.322 10 88.97 0.877 0.205 66.08 0.618 0.541 57.25 0.515 0.459 85.84 0.842 0.295 90.04 0.889 0.442 90.31 0.892 0.172 15 91.74 0.908 0.198 69.45 0.657 0.498 61.26 0.561 0.42 89.54 0.883 0.359 92.49 0.916 0.307 92.76 0.919 0.269 20 92.99 0.922 0.275 70.94 0.674 0.389 63.67 0.589 0.466 90.2 0.891 0.287 93.32 0.926 0.183 93.48 0.927 0.273 25 94.12 0.934 0.14 72.66 0.693 0.287 68.57 0.646 0.341 90.97 0.899 0.439 93.9 0.932 0.253 94.11 0.934 0.306 30 94.44 0.938 0.099 76.54 0.737 0.505 71.17 0.676 0.414 92.2 0.911 0.316 94.51 0.939 0.245 94.44 0.938 0.308
Table 3. Comparison of classification performance of Indian pine dataset
View tableView in ArticleTable 3. Comparison of classification performance of Indian pine dataset
Number
of
bands
Proposed
method（β=0.1）
SVD ID WaLuDi WaLuMI RRS OA KA STD OA KA STD OA KA STD OA KA STD OA KA STD OA KA STD 5 69.61 0.649 0.272 69.88 0.65 0.517 52.46 0.443 0.416 60.59 0.535 0.356 60.9 0.541 0.341 67.03 0.616 0.496 10 80.78 0.78 0.183 80.16 0.773 0.2 55.87 0.483 0.651 75.15 0.714 0.216 77.85 0.745 0.221 75.19 0.714 0.236 15 83.92 0.816 0.237 84.62 0.824 0.222 58.71 0.518 0.584 84.06 0.818 0.232 80.24 0.773 0.17 80 0.771 0.256 20 86.38 0.844 0.242 86.24 0.843 0.219 62.31 0.557 0.465 85.78 0.837 0.223 85.39 0.833 0.206 83.17 0.807 0.408 25 87.42 0.856 0.192 86.92 0.851 0.193 64.93 0.593 0.381 87.75 0.86 0.206 86.44 0.845 0.173 84.83 0.826 0.229 30 88.7 0.871 0.252 87.93 0.862 0.305 69.24 0.647 0.205 88.73 0.871 0.168 87.69 0.859 0.259 86.65 0.847 0.275
Table 4. Comparison of |
z | scores obtainedView tableView in ArticleTable 4. Comparison of |
z | scores obtainedDatasets SVD ID WaLuDi WaLuMI RRS Botswana 3 290.1 1 228.4 175.44 4 675.3 437.5 KSC 6 425.8 12 547.8 2 445.65 131.5 9.67 IP 2 265.6 135 636.3 136.05 19 661.1 19 500
Table 5. Comparison of calculation time
View tableView in ArticleTable 5. Comparison of calculation time
Datasets SVD ID WaLuDi WaLuMI RRS VPRS(β=0.1) Botswana 357.9 656.2 647.8 700.4 514.9 515.8 KSC 316.6 542.3 540.7 585.9 433.6 430.7 IP 21.8 36.9 36.8 39.8 29.5 29.3
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Jing CHEN, Zhenxing ZHANG. Greedy Unsupervised Hyperspectral Image Band Selection Method Based on Variable Precision Rough Set[J]. Acta Photonica Sinica, 2021, 50(2): 103
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Category: Image Processing
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Published Online: Aug. 26, 2021
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