A Novel Spatial Analysis Method for Remote Sensing Image Classification

被引：16

作者：

Gao, Jianqiang ^{[1
]}

Xu, Lizhong ^{[1
]}

机构：

[1] Hohai Univ, Coll Comp & Informat Engn, Nanjing 210098, Jiangsu, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2016年 / 43卷 / 03期

关键词：

Remote sensing image; Projection matrix; Support vector machine; Image classification; Kernel method; KERNEL SPARSE REPRESENTATION; CONTEXTUAL INFORMATION; GIS DATA; SYSTEM; REDUCTION;

D O I：

10.1007/s11063-015-9447-0

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new and efficient classification model is introduced in this paper. The proposed model enjoys the information of null space of within-class and range space of within-class. And the proposed model aims at defining a reliable spatial analysis criterion for the remote sensing image, taking advantage of the differences in different areas. Finally, by incorporating fisher linear discriminant analysis and support vector machine (or K-nearest neighbor) classifier among image pixels, the model obtained more accurate classification results.

引用

页码：805 / 821

页数：17

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