基于上下文敏感贝叶斯网络的角度阈值多元变化检测
|
|
朱睿, 李轶鲲, 李小军, 杨树文, 谢江陵
|
Multivariate alteration detection using angle thresholds based on a context-sensitive Bayesian network
|
|
ZHU Rui, LI Yikun, LI Xiaojun, YANG Shuwen, XIE Jiangling
|
|
表3 研究区3变化检测算法性能比较
|
Tab.3 Performance results of change detection algorithms in study area 3
|
|
| 算法 | 荒地到建筑物 | 建筑物到荒地 | 林地到建筑物 | 错检 率/% | 漏检 率/% | 总体精 度/% | Kappa 系数 | 错检 率/% | 漏检 率/% | 总体精 度/% | Kappa 系数 | 错检 率/% | 漏检 率/% | 总体精 度/% | Kappa 系数 | | 本文算法 | 31.38 | 2.05 | 93.30 | 0.768 1 | 27.96 | 4.76 | 99.45 | 0.817 6 | 26.83 | 18.05 | 99.77 | 0.772 0 | FCM-SBN- CVAPS-AT | 28.37 | 22.19 | 92.42 | 0.701 5 | —① | — | — | — | — | — | — | — | SVM-CVAPS- AT | 33.95 | 12.20 | 91.80 | 0.705 9 | 48.22 | 16.68 | 98.76 | 0.632 8 | — | — | — | — | FCM-CSBN- CVAPS-MC | 8.56 | 63.87 | 91.93 | 0.482 8 | — | — | — | — | — | — | — | — | FCM-SBN- CVAPS-MC | 30.39 | 35.53 | 91.75 | 0.622 3 | — | — | — | — | — | — | — | — | SVM-CVAPS- MC | 17.81 | 36.56 | 93.96 | 0.682 9 | — | — | — | — | — | — | — | — | FCM-CSBN- PCC | 42.86 | 53.06 | 87.38 | 0.443 7 | 93.00 | 86.39 | 96.49 | 0.076 2 | 99.38 | 84.60 | 87.91 | 0.002 9 | | DCVA | 65.70 | 34.33 | 77.10 | 0.323 5 | 98.00 | 65.13 | 76.63 | 0.013 2 | — | — | — | — |
|
|
|