Descriptor Correlation Analysis for Remote Sensing Image Multi-Scale Classification

被引：0

作者：

dos Santos, J. A. ^{[1
,2
]}

Faria, F. A. ^{[1
]}

Torres, R. da S. ^{[1
]}

Rocha, A. ^{[1
]}

Gosselin, P-H.

Philipp-Foliguet, S.

Falcao, A. ^{[1
,2
]}

机构：

[1] Univ Estadual Campinas, Inst Comp, RECOD Lab, Campinas, SP, Brazil

[2] Univ Cergy Pontoise, CNRS, ENSES, ETIS, Cergy, France

来源：

2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012) | 2012年

基金：

巴西圣保罗研究基金会;

关键词：

ENSEMBLES; DIVERSITY;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper addresses the problem of remote sensing image multi-scale classification by: (i) showing that using multiple scales does improve classification results, but not all scales have the same importance; (ii) showing that image descriptors do not offer the same contribution at all scales, as commonly thought, and some of them are very correlated; (iii) introducing a simple approach to automatically select segmentation scales, descriptors, and classifiers based on correlation and accuracy analysis.

引用

页码：3078 / 3081

页数：4

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