Deep Difference Representation Learning for Multi-spectral Imagery Change Detection

DOI

10.2991/icamcs-16.2016.204How to use a DOI?

Keywords

Change Detection, Multi-Spectral Imagery, Difference Representation, Denoising Autoencoder, Deep Learning

Abstract

Change detection is an ongoing hot topic in multi-spectral imagery applications, how to exploit the available spectral information effectively for change detection is still an open question. Considering the noise interference and redundancy of multi-spectral imagery, it is important and necessary to learn more abstract and robust feature from raw spectrums for change detection application. In this paper, a deep difference representation learning model is proposed for multi-spectral change detection. In this model, two stacked denoising autoencoders are established, one for learning more abstract features from raw spectrums blocks, and the other for learning difference representations from the stacked change feature. The former is used to weaken noise interference and reduce redundancy, while the latter has the ability to highlight changes and suppress unchanged pixels. The experimental results on real multi-spectral data demonstrate the feasibility, effectiveness and robustness of the proposed deep difference representation learning model on multi-spectral change detection task.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Hui Zhang
AU  - Puzhao Zhang
PY  - 2016/06
DA  - 2016/06
TI  - Deep Difference Representation Learning for Multi-spectral Imagery Change Detection
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 1008
EP  - 1014
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.204
DO  - 10.2991/icamcs-16.2016.204
ID  - Zhang2016/06
ER  -