Knowledge Commons of Institute of Automation,CAS
SLOW FEATURE ANALYSIS BASED ON CONVOLUTIONAL NEURAL NETWORK FOR SAR IMAGE CHANGE DETECTION | |
Wan L(万玲)1,2![]() ![]() ![]() | |
2021-10-12 | |
会议名称 | 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS |
会议日期 | 11-16 July 2021 |
会议地点 | Brussels, Belgium |
摘要 | Change detection in SAR images is an important but challenge task. Due to the difficulty of SAR interpretation, reliable training samples are lacking, limiting the application of deep learning technology in SAR image change detection. To overcome this problem, this article proposes an unsupervised SAR image change detection method based on slow feature analysis theory with convolutional neural network (SAR-SFAnet). It adopts SDAEs to automatically extract features from SAR data, and employs slow feature analysis theory to project the extracted multi -dimensional features into a new space. In addition, an alternative optimization strategy is introduced, making the features learned by bi - temporal stacked denoising auto-encoder (SDAEs) have more consistent representations, as well as making the change detection map more accurate. Finally, comparative experiments are carried out on two real SAR data sets, demonstrating the effectiveness of the proposed method. |
DOI | 10.1109/IGARSS47720.2021.9553912 |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47535 |
专题 | 复杂系统认知与决策实验室_高效智能计算与学习 |
通讯作者 | Ma L(马雷) |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Beijing University of Technology 4.Harbin University of Science and Technology |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wan L,Ma L,Guo JL,et al. SLOW FEATURE ANALYSIS BASED ON CONVOLUTIONAL NEURAL NETWORK FOR SAR IMAGE CHANGE DETECTION[C],2021. |
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