Next Article in Journal
Retrieval and Comparison of Forest Leaf Area Index Based on Remote Sensing Data from AVNIR-2, Landsat-5 TM, MODIS, and PALSAR Sensors
Next Article in Special Issue
Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area
Previous Article in Journal
Adaptive Surface Modeling of Soil Properties in Complex Landforms
Previous Article in Special Issue
A Formal Framework for Integrated Environment Modeling Systems
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(6), 177;

Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images

College of Computer Science & Technology, Huaqiao University, Xiamen 361021, China
College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
Department of Computer Science and Information Engineering, National Quemoy University, Kinmen 89250, Taiwan
Author to whom correspondence should be addressed.
Academic Editors: Jason C. Hung, Yu-Wei Chan, Neil Y. Yen, Qingguo Zhou and Wolfgang Kainz
Received: 28 March 2017 / Revised: 27 May 2017 / Accepted: 18 June 2017 / Published: 20 June 2017
(This article belongs to the Special Issue Advanced Geo-Information Technologies for Anticipatory Computing)
View Full-Text   |   Download PDF [1557 KB, uploaded 20 June 2017]   |  


Mangroves are valuable contributors to coastal ecosystems, and remote sensing is an indispensable way to obtain knowledge of the dynamics of mangrove ecosystems. Due to the similar spectral features between mangroves and other land cover types, challenges are posed since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this paper, we propose a classification method named the multi-feature joint sparse algorithm (MF-SRU), in which spectral, topographic, and textural features are integrated as the decision-making features, and sparse representation of both center pixels and their eight neighborhood pixels is proposed to represent the spatial correlation of neighboring pixels, which can make good use of the spatial correlation of adjacent pixels. Experiments are performed on Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in Southeastern China, and the results show that the proposed method can effectively improve the extraction accuracy of mangroves. View Full-Text
Keywords: mangroves; remote sensing; multi-feature; joint sparse; Landsat mangroves; remote sensing; multi-feature; joint sparse; Landsat

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).

Share & Cite This Article

MDPI and ACS Style

Luo, Y.-M.; Ouyang, Y.; Zhang, R.-C.; Feng, H.-M. Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images. ISPRS Int. J. Geo-Inf. 2017, 6, 177.

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



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top