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Article

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

1
College of Computer Science & Technology, Huaqiao University, Xiamen 361021, China
2
College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
3
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
ISPRS Int. J. Geo-Inf. 2017, 6(6), 177; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6060177
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)
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
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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. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6060177

AMA 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 International Journal of Geo-Information. 2017; 6(6):177. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6060177

Chicago/Turabian Style

Luo, Yan-Min, Yi Ouyang, Ren-Cheng Zhang, and Hsuan-Ming Feng. 2017. "Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images" ISPRS International Journal of Geo-Information 6, no. 6: 177. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6060177

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