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Change detection based on auto-encoder model for VHR images
Xu Y(徐元); Xiang Shiming; Huo Chunlei; Pan Chunhong; Xu Yuan
2013
Conference NameInternational Symposium on Multispectral Image Processing and Pattern Recognition
Source PublicationMIPPR2013
Conference Date2013-8-10
Conference Place武汉
Publication Place中国
PublisherInternational Society for Optics and Photonics
AbstractChange detection of VHR (Very High Resolution) images is very difficult due to the impacts caused by the seasonal
changes, the imaging condition, and so on. To address the above difficulty, a novel unsupervised change detection
algorithm is proposed based on deep learning, where the complex correspondence between the images is established by
Auto-encoder Model. By taking advantages of the powerful ability of deep learning in compensating the impacts
implicitly, the multi-temporal images can be compared fairly. Experiments demonstrate the effectiveness of the proposed
approach.
KeywordChange Detection Deep Learning Auto-encoder Vhr
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11690
Collection模式识别国家重点实验室_先进数据分析与学习
Corresponding AuthorXu Yuan
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Xu Y,Xiang Shiming,Huo Chunlei,et al. Change detection based on auto-encoder model for VHR images[C]. 中国:International Society for Optics and Photonics,2013.
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