Next Article in Journal
Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data
Previous Article in Journal
Remote Sensing Image Registration with Line Segments and Their Intersections
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(5), 435; doi:10.3390/rs9050435

Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks

College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Zhenhong Li and Prasad S. Thenkabail
Received: 31 December 2016 / Revised: 17 April 2017 / Accepted: 28 April 2017 / Published: 4 May 2017
View Full-Text   |   Download PDF [7125 KB, uploaded 4 May 2017]   |  

Abstract

With the development of Earth observation programs, more and more multi-temporal synthetic aperture radar (SAR) data are available from remote sensing platforms. Therefore, it is demanding to develop unsupervised methods for SAR image change detection. Recently, deep learning-based methods have displayed promising performance for remote sensing image analysis. However, these methods can only provide excellent performance when the number of training samples is sufficiently large. In this paper, a novel simple method for SAR image change detection is proposed. The proposed method uses two singular value decomposition (SVD) analyses to learn the non-linear relations between multi-temporal images. By this means, the proposed method can generate more representative feature expressions with fewer samples. Therefore, it provides a simple yet effective way to be designed and trained easily. Firstly, deep semi-nonnegative matrix factorization (Deep Semi-NMF) is utilized to select pixels that have a high probability of being changed or unchanged as samples. Next, image patches centered at these sample pixels are generated from the input multi-temporal SAR images. Then, we build SVD networks, which are comprised of two SVD convolutional layers and one histogram feature generation layer. Finally, pixels in both multi-temporal SAR images are classified by the SVD networks, and then the final change map can be obtained. The experimental results of three SAR datasets have demonstrated the effectiveness and robustness of the proposed method. View Full-Text
Keywords: change detection; synthetic aperture radar; nonnegative matrix factorization; SVD networks; fuzzy c-means; Deep Semi-NMF change detection; synthetic aperture radar; nonnegative matrix factorization; SVD networks; fuzzy c-means; Deep Semi-NMF
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Gao, F.; Liu, X.; Dong, J.; Zhong, G.; Jian, M. Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks. Remote Sens. 2017, 9, 435.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top