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

页码：1392 / 1396

页数：5