基于宽度学习的非监督SAR影像变化检测

邵攀, 管宗胜, 贾付文

Unsupervised change detection using SAR images based on the broad learning system

SHAO Pan, GUAN Zongsheng, JIA Fuwen

表2 5组数据集上变化检测图的精度指标

Tab.2 Accuracy indicators of change detetion maps on 5 datasets

数据 集 方法 FA MD OE KC IoU A PCAKM 27 4 311 4 338 0.780 1 0.675 9 GaborTLC 5 4 881 4 886 0.747 3 0.634 6 ELM 9 4 801 4 810 0.752 0 0.641 9 PCANet 2 4 717 4 719 0.757 3 0.647 0 CWNN 34 3 946 3 980 0.801 1 0.692 2 RUSACD 8 4 806 4 814 0.751 7 0.640 1 BLS 307 2 532 2 839 0.866 1 0.792 4 B PCAKM 1 602 633 2 235 0.754 0 0.616 7 GaborTLC 1 863 704 2 567 0.724 3 0.580 4 ELM 708 1 176 1 884 0.758 2 0.620 3 PCANet 641 1 235 1 876 0.755 6 0.616 8 CWNN 1 163 695 1 858 0.785 6 0.657 0 RUSACD 538 1 164 1 702 0.777 4 0.644 9 BLS 824 706 1 530 0.816 5 0.698 8 C PCAKM 41 573 7 41 580 0.040 7 0.031 2 GaborTLC 22 024 4 22 026 0.090 1 0.045 8 ELM 6 199 25 6 224 0.258 6 0.175 5 PCANet 12 826 12 12 838 0.155 7 0.094 2 CWNN 13 217 50 13 267 0.146 8 0.089 1 RUSACD 239 176 415 0.849 7 0.741 1 BLS 280 182 462 0.832 8 0.716 2 D PCAKM 65 783 41 65 824 0.016 6 0.008 7 GaborTLC 33 520 80 33 600 0.041 6 0.016 2 ELM 36 336 112 36 448 0.035 7 0.024 4 PCANet 45 968 48 46 016 0.028 9 0.020 1 CWNN 17 072 384 17 456 0.055 7 0.034 7 RUSACD 288 256 544 0.714 1 0.557 5 BLS 181 208 389 0.806 5 0.677 4 E PCAKM 430 724 172 693 603 417 0.520 7 0.424 0 GaborTLC 231 003 212 652 443 655 0.590 4 0.476 7 ELM 444 164 175 766 619 930 0.513 4 0.416 1 PCANet 370 534 171 819 542 353 0.553 8 0.450 7 CWNN 498 038 150 816 648 854 0.510 1 0.418 0 RUSACD 214 101 251 318 465 419 0.553 5 0.435 9 BLS 36 699 163 666 200 365 0.794 8 0.693 4