Simplified Conditional Random Fields With Class Boundary Constraint for Spectral-Spatial Based Remote Sensing Image Classification

被引：49

作者：

Zhang, Guangyun ^{[1
]}

Jia, Xiuping ^{[1
]}

机构：

[1] Univ New S Wales, Univ Coll, Sch Engn & Informat Technol, Canberra, ACT 2600, Australia

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2012年 / 9卷 / 05期

关键词：

Conditional random fields (CRFs); contextual information; Markov random field (MRF); spectral-spatial classification; HYPERSPECTRAL DATA; RELAXATION;

D O I：

10.1109/LGRS.2012.2186279

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：

Conditional random fields (CRF) have been introduced to remote sensing image classification recently to integrate contextual information into remote sensing classification. It employs the spatial property on both pixel's spectral data and labels. However, this leads to a large number of model parameters to train. In this letter, the training efficiency is improved by modifying the conventional CRF model. At the same time, a class boundary constraint is imposed into this framework to avoid over correction. The advantages of the developed method are demonstrated in the experimental results using real remotely sensed data.

引用

页码：856 / 860

页数：5

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