Change detection based on auto-encoder model for VHR images

Yuan Xu; Shiming Xiang; Chunlei Huo; Chunhong Pan

doi:10.1117/12.2031104

27 October 2013 Change detection based on auto-encoder model for VHR images

Yuan Xu, Shiming Xiang, Chunlei Huo, Chunhong Pan

Proceedings Volume 8919, MIPPR 2013: Pattern Recognition and Computer Vision; 891902 (2013) https://doi.org/10.1117/12.2031104
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

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.

Citation Download Citation

Yuan Xu, Shiming Xiang, Chunlei Huo, and Chunhong Pan "Change detection based on auto-encoder model for VHR images", Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891902 (27 October 2013); https://doi.org/10.1117/12.2031104

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