Change detection based on conditional random field models

被引：0

作者：

Chen, Keming ^{[1
]}

Zhou, Zhixin ^{[1
]}

Lu, Hanqing ^{[1
]}

Huo, Chunlei ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China

来源：

CHALLENGES IN REMOTE SENSING: PROCEEDINGS OF THE 3RD WSEAS INTERNATIONAL CONFERENCE ON REMOTE SENSING (REMOTE '07) | 2007年

关键词：

change detection; conditional random fields (CRFs); classification; remote sensing; optical images; feature function;

D O I：

暂无

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

This paper addresses the problem of optical remote sensing images change detection based on conditional random field (CRF) models. CRF, a framework for building probabilistic models, offer several advantages over hidden Markov models for change detection. In this paper, we use the CRF to model the observed images and focus on analyzing the change detection by classifying the pixels of difference image to two different tpyes. Experimental results confirm the effectiveness of the proposed approach.

引用

页码：93 / 97

页数：5

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