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

ISPRS International Journal of Geo-Information

Hsuan-Ming Feng¹  Yi Ouyang¹  Ren-Cheng Zhang²  Yan-Min Luo² 

[1] Technology, Huaqiao University, Xiamen 361021, China;;College of Computer Science &

关键词: mangroves;    remote sensing;    multi-feature;    joint sparse;    Landsat;   

DOI  :  10.3390/ijgi6060177

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown