Robust unsupervised small area change detection from SAR imagery using deep learning
文献类型：期刊论文
| 作者 | Zhang, Xinzheng1,2; Su, Hang1; Zhang, Ce3,4; Gu, Xiaowei5; Tan, Xiaoheng1,2; Atkinson, Peter M.3,6,7 |
| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 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. |
| WOS关键词 | RATIO APPROACH ; NETWORK |
| 资助项目 | National Science Foundation of China[61301224] ; Chongqing Basic and Frontier Research Project[cstc2017jcyjA1378] |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000640986100006 |
| 出版者 | ELSEVIER |
| 资助机构 | National Science Foundation of China ; Chongqing Basic and Frontier Research Project |
| 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.Chongqing Univ, Sch Microelect & Commun Engn, Chongqing 400044, Peoples R China 2.Chongqing Key Lab Space Informat Network & Intell, Chongqing 400044, Peoples R China 3.Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England 4.UK Ctr Ecol & Hydrol, Lib Ave, Lancaster LA1 4AP, England 5.Aberystwyth Univ, Dept Comp Sci, Aberystwyth SY23 3DB, Dyfed, Wales 6.Univ Southampton, Geog & Environm Sci, Southampton SO17 1BJ, Hants, England 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。