Share Email Print
cover

Proceedings Paper

Change detection based on auto-encoder model for VHR images
Author(s): Yuan Xu; Shiming Xiang; Chunlei Huo; Chunhong Pan
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 27 October 2013
PDF: 7 pages
Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891902 (27 October 2013); doi: 10.1117/12.2031104
Show Author Affiliations
Yuan Xu, National Lab. of Pattern Recognition (China)
Shiming Xiang, National Lab. of Pattern Recognition (China)
Chunlei Huo, National Lab. of Pattern Recognition (China)
Chunhong Pan, National Lab. of Pattern Recognition (China)


Published in SPIE Proceedings Vol. 8919:
MIPPR 2013: Pattern Recognition and Computer Vision
Zhiguo Cao, Editor(s)

© SPIE. Terms of Use
Back to Top