Class-Specific Feature Selection With Local Geometric Structure and Discriminative Information Based on Sparse Similar Samples

被引：9
|
作者：
Chen, Xi [1 ]
Gu, Yanfeng [1 ]
机构：
[1] Harbin Inst Technol, Sch Elect & Informat Engn, Dept Informat Engn, Harbin 150001, Peoples R China
来源：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2015年 / 12卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Class-based features; object-oriented image analysis; remote sensing; supervised feature selection; CLASSIFICATION;
D O I：
10.1109/LGRS.2015.2402205
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
It is necessary while quite challenging to select features strongly relevant to a thematic class, i.e., class-specific features, from very high resolution (VHR) remote sensing images. To meet this challenge, a class-specific feature selection method based on sparse similar samples (CFS4) is proposed. Specifically, CFS4 incorporates the local geometrical structure and discriminative information of the data into a sparsity regularization problem. The experimental results on VHR satellite images well validate the effectiveness and practicability of the proposed method.
引用
收藏
页码：1392 / 1396
页数：5
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