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

Author

Xi Chen ; Yanfeng Gu

Author_Institution

Dept. of Inf. Eng., Harbin Inst. of Technol., Harbin, China

Volume

12

Issue

7

fYear

2015

fDate

42186

Firstpage

1392

Lastpage

1396

Abstract

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.

Keywords

artificial satellites; feature selection; geophysical image processing; image resolution; remote sensing; CFS4; VHR remote sensing images; VHR satellite image; class specific feature selection method based on sparse similar sample; discriminative information; geometrical structure; sparsity regularization problem; very high resolution; Accuracy; Computational modeling; Feature extraction; Information retrieval; Object oriented modeling; Remote sensing; Support vector machines; Class-based features; object-oriented image analysis; remote sensing; supervised feature selection;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2015.2402205

Filename

7060695