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顾及冲突分析的模糊积分非监督变化检测

邵攀 任东 董婷

邵攀, 任东, 董婷. 顾及冲突分析的模糊积分非监督变化检测. 自动化学报, 2021, 47(9): 2250−2263 doi: 10.16383/j.aas.c190047
引用本文: 邵攀, 任东, 董婷. 顾及冲突分析的模糊积分非监督变化检测. 自动化学报, 2021, 47(9): 2250−2263 doi: 10.16383/j.aas.c190047
Shao Pan, Ren Dong, Dong Ting. Unsupervised change detection based on fuzzy integral considering conflict analysis. Acta Automatica Sinica, 2021, 47(9): 2250−2263 doi: 10.16383/j.aas.c190047
Citation: Shao Pan, Ren Dong, Dong Ting. Unsupervised change detection based on fuzzy integral considering conflict analysis. Acta Automatica Sinica, 2021, 47(9): 2250−2263 doi: 10.16383/j.aas.c190047

顾及冲突分析的模糊积分非监督变化检测

doi: 10.16383/j.aas.c190047
基金项目: 国家自然科学基金 (41901341, 41701504), 国家重点研发计划(2016YFD0800902), 湖北省技术创新专项(重大项目) (2017ABA157), 地理国情监测国家测绘地理信息局重点实验室开放基金项目(2017NGCM04)资助
详细信息
    作者简介:

    邵攀:三峡大学计算机与信息学院讲师. 2016年获得武汉大学博士学位. 主要研究方向为遥感图像处理, 变化检测. E-mail: panshao@whu.edu.cn

    任东:三峡大学计算机与信息学院教授. 主要研究方向为人工智能, 模式识别, 3S技术. E-mail: rendong5227@163.com

    董婷:三峡大学计算机与信息学院讲师. 2016年获得武汉大学博士学位. 主要研究方向为3S技术, 灾害监测. 本文通信作者. E-mail: dongt@ctgu.edu.cn

Unsupervised Change Detection Based on Fuzzy Integral Considering Conflict Analysis

Funds: Supported by National Natural Science Foundation of China (41901341, 41701504), National Key Research and Development Program of China (2016YFD0800902), Major Technological Innovation Project in Hubei Province of China (2017ABA157), and Open Project Program of Key Laboratory for National Geographic Census and Monitoring of National Administration of Surveying, Mapping and Geoinformation (2017NGCM04)
More Information
    Author Bio:

    SHAO Pan Lecturer at the College of Computer and Information Technology, China Three Gorges University. He received his Ph.D. degree from Wuhan University in 2016. His received interest covers remote sensing image processing and change detection

    REN Dong Professor at the College of Computer and Information Technology, China Three Gorges University. His research interest covers artificial intelligence, pattern recognition, and 3S technology

    DONG Ting Lecturer at the College of Computer and Information Technology, China Three Gorges University. She received her Ph.D. degree from Wuhan University in 2016. Her research interest covers 3S technology and disaster monitoring. Corresponding author of this paper

  • 摘要: 以模糊积分(Fuzzy integral, FI)为基础, 提出一种顾及冲突分析(Conflict analysis, CA)的全自动遥感变化检测方法CA-based FI, CAFI). CAFI首先选取典型的对比算子, 生成信息互补的差异图(Difference image, DI)集; 其次利用模糊聚类、杰卡德相似系数和FI对差异图进行决策级融合, 得到初步融合变化检测图; 然后通过模糊集理论计算像元的信息冲突程度, 将初步融合检测结果自适应地划分为冲突严重区域和冲突较弱区域; 最后, 对冲突较弱的像元, 将其初步融合结果作为最终检测结果, 对易产生融合错误的冲突严重像元, 利用地统计分析对其重新分类. CAFI能够集成不同信息优势的同时, 很大程度地解决FI融合过程中的信息冲突问题. 三组真实遥感数据的实验结果验证了CAFI的有效性和鲁棒性.
  • 图  1  所提出的CAFI融合模型的基本流程

    Fig.  1  Flowchart of the proposed CAFI fusion model

    图  2  初步融合步骤的基本流程

    Fig.  2  Flowchart of the preliminary fusion step

    图  3  半径$r = 3$的克里金窗口

    Fig.  3  Kriging window with radius $r = 3$

    图  4  实验1中使用的遥感影像及其变化参考图

    Fig.  4  The remote sensing images used in Experiment 1 and its reference map

    图  5  实验2中使用的遥感影像及其变化参考图

    Fig.  5  The remote sensing images used in Experiment 2 and its reference map

    图  6  实验3中使用的遥感影像及其变化参考图

    Fig.  6  The remote sensing images used in Experiment 3 and its reference map

    图  7  参数分析图 ((a) ~ (c) Kappa系数随参数$T_1$$T_2$的变化曲面; (d) Kappa系数随半径$r$的变化曲线)

    Fig.  7  Diagram of parameter analysis ((a) ~ (c) changing surface of Kappa coefficients with parameters $T_1$ and $T_2$ and (d) relationships between KC and radius $r$ for the three datasets)

    图  8  通过CVA、SCM、PCA和SGD得到的第1组实验数据的4组差异图

    Fig.  8  The four difference images obtained by CVA, SCM, PCA, and SGD on Dataset 1

    图  9  不同检测技术对第1组实验数据的变化检测结果

    Fig.  9  Change detection results obtained by different methods on Dataset 1

    图  10  不同检测技术对第2组实验数据的变化检测结果

    Fig.  10  Change detection results obtained by different methods on Dataset 2

    图  11  不同检测技术对第3组实验数据的变化检测结果

    Fig.  11  Change detection results obtained by different methods on Dataset 3

    图  12  影像块A及其变化检测结果

    Fig.  12  Image blocks A and its change detection results on Dataset 3

    图  13  影像块F 及其变化检测结果

    Fig.  13  Image blocks F and its change detection results on Dataset 3

    表  1  波谱曲线的变化类型

    Table  1  The change categories of spectral curve

    变化类型具有性质
    1a
    2b
    3c
    4a, b
    5a, c
    6b, c
    7a, b, c
    下载: 导出CSV

    表  2  第1组实验数据变化检测结果的定量分析指标

    Table  2  Quantitative analysis indices for change detection results on Dataset 1

    方法MDFAOEKappa
    CVA3 40087 83591 2350.6037
    SCM12 2801 17413 4540.9067
    PCA11 5347 98919 5230.8708
    SGD5 52112 95618 4770.8852
    Optimal-T13 99419 21533 2090.7911
    RFLICM4 35653 10257 4580.7099
    FLGICM4 00028 41232 4120.8161
    HFV4 59431 55636 1500.7975
    MV5 70316 35422 0570.8654
    KMAMV5 45410 22015 6740.9011
    FI5 88412 37918 2630.8859
    CAFI3 3312 6075 9380.9613
    下载: 导出CSV

    表  3  第2组实验数据变化检测结果的定量分析指标

    Table  3  Quantitative analysis indices for change detection results on Dataset 2

    方法MDFAOEKappa
    CVA14 85420 30135 1550.8335
    SCM34 3191 75836 0770.8047
    PCA41 7243 68145 4050.7491
    SGD15 27014 63529 9050.8555
    Optimal-T19 35313 19932 5520.8397
    RFLICM15 67616 38932 0650.8458
    FLGICM18 4018 83927 2400.8643
    HFV14 22314 17228 3950.8631
    MV14 38614 42528 8110.8611
    KMAMV17 22210 44227 6640.8635
    FI23 5403 69527 2350.8593
    CAFI11 9767 00918 9850.9069
    下载: 导出CSV

    表  4  第3组实验数据变化检测结果的定量分析指标

    Table  4  Quantitative analysis indices for change detection results on Dataset 3

    方法MDFAOEKappa
    CVA98 60342 243140 8460.7462
    SCM149 6086 455156 0630.7037
    PCA137 46514 987152 4520.7142
    SGD86 75653 509140 2650.7506
    Optimal-T70 01062 914132 9240.7671
    RFLICM101 18037 026138 2060.7498
    FLGICM147 67817 094164 7720.6896
    HFV93 14833 121126 2690.7719
    MV93 98639 105133 0910.7603
    KMAMV114 73726 485141 2220.7406
    FI111 34212 559123 9010.7710
    CAFI67 26130 53897 7990.8257
    下载: 导出CSV

    表  5  实验2中的冲突严重像元的变化检测结果

    Table  5  Change detection results on strongly conflicting pixels in Experiment 2

    方法$OE$$Con_{OE}$$P_{Con_{OE} }\;({\text{%} })$${Con_A}\;({\text{%} })$
    CVA35 15527 08677.0469.03
    SCM36 07719 83754.9977.32
    PCA45 40518 74741.2978.56
    SGD29 90522 08873.8674.75
    Optimal-T32 55223 13571.0773.55
    RFLICM32 06523 68473.8672.92
    FLGICM27 24018 29167.1579.09
    HFV28 39520 85773.4576.15
    MV28 81121 72475.4075.16
    KMAMV27 66418 91368.3778.38
    FI27 23520 77976.2976.24
    CAFI18 98512 52985.68
    下载: 导出CSV
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  • 收稿日期:  2019-01-18
  • 录用日期:  2019-06-06
  • 网络出版日期:  2021-10-13
  • 刊出日期:  2021-10-13

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