Next Article in Journal
Measurement of Walking Ground Reactions in Real-Life Environments: A Systematic Review of Techniques and Technologies
Next Article in Special Issue
A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery
Previous Article in Journal
Silicon Nitride Photonic Integration Platforms for Visible, Near-Infrared and Mid-Infrared Applications
Previous Article in Special Issue
Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(9), 2087; https://doi.org/10.3390/s17092087

A Robust Sparse Representation Model for Hyperspectral Image Classification

1
Department of Telecommunications and Information Processing, Ghent University, Sint Pietersnieuwstraat 41, 9000 Gent, Belgium
2
The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan 430079, China
This paper is an extended version of our paper published in ICIP 2017, “Robust Joint Sparsity Model for Hyperspectral Image Classification”.
*
Author to whom correspondence should be addressed.
Received: 10 August 2017 / Revised: 28 August 2017 / Accepted: 7 September 2017 / Published: 12 September 2017
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
View Full-Text   |   Download PDF [1605 KB, uploaded 12 September 2017]   |  

Abstract

Sparse representation has been extensively investigated for hyperspectral image (HSI) classification and led to substantial improvements in the performance over the traditional methods, such as support vector machine (SVM). However, the existing sparsity-based classification methods typically assume Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust classification model that admits realistic mixed noise, which includes Gaussian noise and sparse noise. We combine a model for mixed noise with a prior on the representation coefficients of input data within a unified framework, which produces three kinds of robust classification methods based on sparse representation classification (SRC), joint SRC and joint SRC on a super-pixels level. Experimental results on simulated and real data demonstrate the effectiveness of the proposed method and clear benefits from the introduced mixed-noise model. View Full-Text
Keywords: robust classification; hyperspectral image; super-pixel segmentation; sparse representation robust classification; hyperspectral image; super-pixel segmentation; sparse representation
Figures

Figure 1

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

Huang, S.; Zhang, H.; Pižurica, A. A Robust Sparse Representation Model for Hyperspectral Image Classification. Sensors 2017, 17, 2087.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top