Change detection based on auto-encoder model for VHR images

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Change detection based on auto-encoder model for VHR images
	Xu Y(徐元); Xiang Shiming; Huo Chunlei; Pan Chunhong; Xu Yuan
	2013
会议名称	International Symposium on Multispectral Image Processing and Pattern Recognition
会议录名称	MIPPR2013
会议日期	2013-8-10
会议地点	武汉
出版地	中国
出版者	International Society for Optics and Photonics
摘要	Change 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.
关键词	Change Detection Deep Learning Auto-encoder Vhr
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11690
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
通讯作者	Xu Yuan
作者单位	中国科学院自动化研究所
推荐引用方式 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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