Robust unsupervised small area change detection from SAR imagery using deep learning

doi:10.1016/j.isprsjprs.2021.01.004

	Robust unsupervised small area change detection from SAR imagery using deep learning
	Zhang, Xinzheng 2,3; Su, Hang 2; Zhang, Ce 4,5; Gu, Xiaowei 6; Tan, Xiaoheng 2,3; Atkinson, Peter M.1,4,7
刊名	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
	2021-03-01
卷号	173 页码:79-94
关键词	Change detection Synthetic aperture radar Difference image Fuzzy c-means algorithm Deep learning
ISSN号	0924-2716
DOI	10.1016/j.isprsjprs.2021.01.004
通讯作者	Zhang, Xinzheng(zhangxinzheng03@126.com) ; Zhang, Ce(c.zhang9@lancaster.ac.uk)
英文摘要	Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection.
资助项目	National Science Foundation of China[61301224] ; Chongqing Basic and Frontier Research Project[cstc2017jcyjA1378]
WOS关键词	RATIO APPROACH ; NETWORK
WOS研究方向	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000640986100006
资助机构	National Science Foundation of China ; Chongqing Basic and Frontier Research Project
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/161626]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Xinzheng; Zhang, Ce
作者单位	1.Univ Southampton, Geog & Environm Sci, Southampton SO17 1BJ, Hants, England 2.Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China 3.Chongqing Key Lab Space Informat Network & Intell, Chongqing 400044, Peoples R China 4.Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England 5.UK Ctr Ecol & Hydrol, Lib Ave, Lancaster LA1 4AP, England 6.Aberystwyth Univ, Dept Comp Sci, Aberystwyth SY23 3DB, Dyfed, Wales 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xinzheng,Su, Hang,Zhang, Ce,et al. Robust unsupervised small area change detection from SAR imagery using deep learning[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2021,173:79-94.
APA	Zhang, Xinzheng,Su, Hang,Zhang, Ce,Gu, Xiaowei,Tan, Xiaoheng,&Atkinson, Peter M..(2021).Robust unsupervised small area change detection from SAR imagery using deep learning.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,173,79-94.
MLA	Zhang, Xinzheng,et al."Robust unsupervised small area change detection from SAR imagery using deep learning".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 173(2021):79-94.