Next Article in Journal
Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification
Previous Article in Journal
Reply to Kern, C. The Difficulty of Measuring the Absorption of Scattered Sunlight by H2O and CO2 in Volcanic Plumes: A Comment on Pering, et al. “A Novel and Inexpensive Method for Measuring Volcanic Plume Water Fluxes at High Temporal Resolution”, Remote Sens. 2017, 9, 146
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(10), 1044; https://doi.org/10.3390/rs9101044

Spectrally-Spatially Regularized Low-Rank and Sparse Decomposition: A Novel Method for Change Detection in Multitemporal Hyperspectral Images

1,* and 2,3
1
School of Computer Science and Technology, Donghua University, Shanghai 201620, China
2
Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University,Shanghai 200433, China
3
Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China
*
Author to whom correspondence should be addressed.
Received: 30 August 2017 / Revised: 8 October 2017 / Accepted: 9 October 2017 / Published: 12 October 2017
(This article belongs to the Section Remote Sensing Image Processing)
Full-Text   |   PDF [5213 KB, uploaded 17 October 2017]   |  

Abstract

Change detection (CD) for multitemporal hyperspectral images (HSI) can be approached as classification consisting of two steps, change feature extraction and change identification. This paper is focused on binary classification of the changed and the unchanged samples, which is the essential case of change detection. Meanwhile, it is challenging to extract clean change features from heavily corrupted spectral change vectors (SCV) of multitemporal HSI. The corruptions can be characterized as gross sample-specific errors, i.e., outliers, and small entry-wise noise following Gaussian distribution. To address the issue, this paper proposes a novel Spectrally-Spatially (SS) Regularized Low-Rank and Sparse Decomposition (LRSD) model, denoted by LRSD_SS. It decomposes the SCV into three components, a locally smoothed low-rank matrix for the clean change features, a sparse matrix for the outliers and an error matrix for the small Gaussian noise. The proposed method is effective in change feature extraction and robust to noise corruptions as it exploits the underlying data structures of the SCV, especially local spectral-spatial smoothness. It is also efficient since there is a closed-form solution for the feature component in the optimization problem of LRSD_SS. The experimental results in the paper show that the proposed method outperforms several classic methods which only deal with the spectral domain of image samples, as well as some state-of-the-art methods which use both spectral and spatial information View Full-Text
Keywords: change detection; classification; feature extraction; low-rank and sparse decomposition; spectral-spatial regularization; outlier; robustness change detection; classification; feature extraction; low-rank and sparse decomposition; spectral-spatial regularization; outlier; robustness
Figures

Graphical abstract

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

Share & Cite This Article

MDPI and ACS Style

Chen, Z.; Wang, B. Spectrally-Spatially Regularized Low-Rank and Sparse Decomposition: A Novel Method for Change Detection in Multitemporal Hyperspectral Images. Remote Sens. 2017, 9, 1044.

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