Change Vector Analysis in Posterior Probability Space: A New Method for Land Cover Change Detection

被引：126

作者：

Chen, Jin ^{[1
]}

Chen, Xuehong ^{[1
]}

Cui, Xihong ^{[1
]}

Chen, Jun ^{[2
]}

机构：

[1] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China

[2] Natl Geoinformat Ctr China, Beijing 100255, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2011年 / 8卷 / 02期

关键词：

Change vector analysis (CVA); land cover change; postclassification comparison (PCC); posterior probability space; RADIOMETRIC NORMALIZATION; CLASSIFICATION; IMAGERY;

D O I：

10.1109/LGRS.2010.2068537

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：

Postclassification comparison (PCC) and change vector analysis (CVA) have been widely used for land use/cover change detection using remotely sensed data. However, PCC suffers from error cumulation stemmed from an individual image classification error, while a strict requirement of radiometric consistency in remotely sensed data is a bottleneck of CVA. This letter proposes a new method named CVA in posterior probability space (CVAPS), which analyzes the posterior probability by using CVA. The CVAPS approach was applied and validated by a case study of land cover change detection in Shunyi District, Beijing, China, based on multitemporal Landsat Thematic Mapper data. Accuracies of "change/no-change" detection and "from-to" types of change were assessed. The results show that error cumulation in PCC was reduced in CVAPS. Furthermore, the main drawbacks in CVA were also alleviated effectively by using CVAPS. Therefore, CVAPS is potentially useful in land use/cover change detection.

引用

页码：317 / 321

页数：5

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