Subpixel land cover mapping based on multi-dictionary sparse Representation for remote sensing images

被引：0

作者：

Huang, Hui-Juan ^{[1
]}

Yu, Jing ^{[1
]}

Sun, Wei-Dong ^{[1
]}

机构：

[1] Dept. of Electronic Engineering, Tsinghua University, Beijing

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2015年 / 43卷 / 06期

关键词：

Multi-dictionary learning; Sparse representation; Spatial dependence; Spectral unmixing; Subpixel mapping;

D O I：

10.3969/j.issn.0372-2112.2015.06.001

中图分类号：

学科分类号：

摘要：

This paper proposes a subpixel land cover mapping method based on multi-dictionary sparse representation. In this method, some known high spatial resolution land cover maps are used to formulate different dictionaries that represent distribution modes of different land cover classes, the unclassified subpixels are represented by each dictionary, and they are also classified according to the principle of reconstruction-error minimization and spectral distortion constraint. Experimental results both on artificial and real images show that the method deals with the diversity between different distribution modes of different land cover classes effectively, and achieves higher subpixel mapping accuracy and robustness than the other related methods. ©, 2015, Chinese Institute of Electronics. All right reserved.

引用

页码：1041 / 1049

页数：8

共 26 条

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