Next Article in Journal
Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series
Previous Article in Journal
Greening and Browning of the Hexi Corridor in Northwest China: Spatial Patterns and Responses to Climatic Variability and Anthropogenic Drivers
Previous Article in Special Issue
A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(8), 1271; https://doi.org/10.3390/rs10081271

Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs

1,2
,
1,2
,
1,2,* and 3
1
College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2
Qingdao Key Laboratory of Mixed Reality and Virtual Ocean, Ocean University of China, Qingdao 266100, China
3
Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Received: 10 July 2018 / Revised: 5 August 2018 / Accepted: 9 August 2018 / Published: 12 August 2018
View Full-Text   |   Download PDF [1767 KB, uploaded 12 August 2018]   |  

Abstract

Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods. View Full-Text
Keywords: random multi-graphs; local binary patterns; hyperspectral image; pattern classification random multi-graphs; local binary patterns; hyperspectral image; pattern classification
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

Gao, F.; Wang, Q.; Dong, J.; Xu, Q. Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs. Remote Sens. 2018, 10, 1271.

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