Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images
AbstractMangroves 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
Share & Cite This Article
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.
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.Chicago/Turabian Style
Luo, Yan-Min; Ouyang, Yi; Zhang, Ren-Cheng; Feng, Hsuan-Ming. 2017. "Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images." ISPRS Int. J. Geo-Inf. 6, no. 6: 177.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.