Change-detection Method for SAR Image Using Adaptive Distance and Fuzzy Topology Optimization-based Fuzzy Clustering

doi:10.11947/j.AGCS.2018.20160607

Abstract

Abstract: In this paper, a framework of change detection based on adaptive distance and fuzzy topology (FATCD) is proposed for synthetic aperture radar (SAR) imagery. FATCD integrates the characteristics of differenced image and can overcome the limitations of fuzzy C-means (FCM) type algorithms. The framework includes two key steps. First, a new adaptive method is employed to calculate the distances from samples to cluster centers using an adaptive distance function. As a result, the formula of pixel membership evaluation is modified, and the accuracy of the obtained fuzzy membership degree is improved. Then, fuzzy topology is integrated into the maximum membership rule to improve the traditional defuzzification method. In virtue of the above two points, FATCD can enhance the change detection performance of FCM-type algorithms. Experimental results on two different SAR images confirm the effectiveness of the proposed technique.

Key words: SAR image change detection, fuzzy topology, adaptive distance, fuzzy clustering algorithm, FATCD

CLC Number:

P237

WANG Jianming, SHI Wenzhong, SHAO Pan. Change-detection Method for SAR Image Using Adaptive Distance and Fuzzy Topology Optimization-based Fuzzy Clustering[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(5): 611-619.

References

[1] 公茂果, 苏临之, 李豪, 等. 合成孔径雷达影像变化检测研究进展[J]. 计算机研究与发展, 2016, 53(1):123-137. GONG Maoguo, SU Linzhi, LI Hao, et al. A Survey on Change Detection in Synthetic Aperture Radar Imagery[J]. Journal of Computer Research and Development, 2016, 53(1):123-137.
[2] 胡召玲. 广义高斯模型及KI双阈值法的SAR图像非监督变化检测[J]. 测绘学报, 2013, 42(1) 116-122. HU Zhaoling. An Unsupervised Change Detection Approach Based on KI Dual Thresholds under the Generalized Gauss Model Assumption in SAR Images[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1):116-122.
[3] 庄会富, 邓喀中, 范洪冬. 纹理特征向量与最大化熵法相结合的SAR影像非监督变化检测[J]. 测绘学报, 2016, 45(3):339-346. DOI:10.11947/j.AGCS.2016.20150022. ZHUANG Huifu, DENG Kazhong, FAN Hongdong. SAR Images Unsupervised Change Detection Based on Combination of Texture Feature Vector with Maximum Entropy Principle[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(3):339-346. DOI:10.11947/j.AGCS.2016.20150022.
[4] 郝洪美, 张永红, 石海燕, 等. 统计假设检验方法在全极化SAR变化检测中的应用[J]. 遥感学报, 2012, 16(3):520-532. HAO Hongmei, ZHANG Yonghong, SHI Haiyan, et al. Application of Test Statistic Method in Fully Polarimtric SAR Change Detection[J]. Journal of Remote Sensing, 2012, 16(3):520-532.
[5] GONG Maoguo, ZHOU Zhiqiang, MA Jingjing. Change Detection in Synthetic Aperture Radar Images Based on Image Fusion and Fuzzy Clustering[J]. IEEE Transactions on Image Processing, 2012, 21(4):2141-2151.
[6] 万红林, 焦李成, 王桂婷, 等. 在感兴趣的区域层面上进行SAR图像变化检测的方法研究[J]. 测绘学报, 2012, 41(2):239-245. WAN Honglin, JIAO Licheng, WANG Guiting, et al. A Region-of-Interest Level Method for Change Detection in SAR Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2):239-245.
[7] 黄世奇, 刘代志, 胡明星, 等. 基于小波变换的多时相SAR图像变化检测技术[J]. 测绘学报, 2010, 39(2):180-186. HUANG Shiqi, LIU Daizhi, HU Mingxing, et al. Multi-Temporal SAR Image Change Detection Technique Based on Wavelet Transform[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(2):180-186.
[8] BRUZZONE L, PRIETO D F. Automatic Analysis of the Difference Image for Unsupervised Change Detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(3):1171-1182.
[9] GONG Maoguo, SU Linzhi, JIA Meng, et al. Fuzzy Clustering with a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images[J]. IEEE Transactions on Fuzzy Systems, 2014, 22(1):98-109.
[10] BEZDEK J C. Pattern Recognition with Fuzzy Objective Function Algorithms[M]. New York:Plenum Press, 1981:65-80.
[11] KRINIDIS S, CHATZIS V. A Robust Fuzzy Local Information C-means Clustering Algorithm[J]. IEEE Transactions on Image Processing, 2010, 19(5):1328-1337.
[12] SHAO Pan, SHI Wenzhong, HE Pengfei, et al. Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm[J]. Remote Sensing, 2016, 8(3):264.
[13] GHOSH A, MISHRA N S, GHOSH S. Fuzzy Clustering Algorithms for Unsupervised Change Detection in Remote Sensing Images[J]. Information Sciences, 2011, 181(4):699-715.
[14] MA Jingjing, GONG Maoguo, ZHOU Zhiqiang. Wavelet Fusion on Ratio Images for Change Detection in SAR Images[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(6):1122-1126.
[15] 崔莹, 熊博莅, 蒋咏梅, 等. 结合结构相似度的自适应多尺度SAR图像变化检测[J]. 中国图象图形学报, 2014, 19(10):1507-1513. CUI Ying, XIONG Boli, JIANG Yongmei, et al. Multi-Scale Approach Based on Structure Similarity for Change Detection in SAR Images[J]. Journal of Image and Graphics, 2014, 19(10):1507-1513.
[16] MISHRA N S, GHOSH S, GHOSH A. Fuzzy Clustering Algorithms Incorporating Local Information for Change Detection in Remotely Sensed Images[J]. Applied Soft Computing, 2012, 12(8):2683-2692.
[17] HAO Ming, ZHANG Hua, SHI Wenzhong, et al. Unsupervised Change Detection Using Fuzzy C-means and MRF from Remotely Sensed Images[J]. Remote Sensing Letters, 2013, 4(12):1185-1194.
[18] CAI Weiling, CHEN Songcan, ZHANG Daoqiang. Fast and Robust Fuzzy C-means Clustering Algorithms Incorporating Local Information for Image Segmentation[J]. Pattern Recognition, 2007, 40(3):825-838.
[19] BOUCHACHIA A, PEDRYCZ W. Enhancement of Fuzzy Clustering by Mechanisms of Partial Supervision[J]. Fuzzy Sets and Systems, 2006, 157(13):1733-1759.
[20] LIU Yingming, LUO Maokang. Fuzzy Topology[M]. Singapore:World Scientific, 1997:40-79.
[21] SHI Wenzhong, SHAO Pan, HAO Ming, et al. Fuzzy Topology-Based Method for Unsupervised Change Detection[J]. Remote Sensing Letters, 2016, 7(1):81-90.
[22] LIU K, SHI Wenzhong. Computing the Fuzzy Topological Relations of Spatial Objects Based on Induced Fuzzy Topology[J]. International Journal of Geographical Information Science, 2006, 20(8):857-883.
[23] PATRA S, GHOSH S, GHOSH A. Histogram Thresholding for Unsupervised Change Detection of Remote Sensing Images[J]. International Journal of Remote Sensing, 2011, 32(21):6071-6089.
[24] BOVOLO F, BRUZZONE L, MARCONCINI M. A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(7):2070-2082.
[25] CONGALTON R G. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data[J]. Remote Sensing of Environment, 1991, 37(1):35-46.