A toolbox for unsupervised change detection analysis

被引：23
|
作者：
Falco, N. [1 ]
Marpu, P. R. [2 ]
Benediktsson, J. A. [1 ]
机构：
[1] Univ Iceland, Fac Elect & Comp Engn, Reykjavik, Iceland
[2] Masdar Inst Sci & Technol, Inst Ctr Water & Environm, Abu Dhabi, U Arab Emirates
来源：
INTERNATIONAL JOURNAL OF REMOTE SENSING | 2016年 / 37卷 / 07期
关键词：
CHANGE VECTOR ANALYSIS; IR-MAD; IMAGE;
D O I：
10.1080/01431161.2016.1154226
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
The analysis of multi-temporal remote-sensing images is one of the main applications in Earth's observation and monitoring. In this paper, we present a Matlab toolbox for change detection analysis of optical multi-temporal remote-sensing data in which unsupervised approaches, iterative principal component analysis (ITPCA), and iteratively reweighted multivariate alteration detection (IR-MAD) are implemented and optimized. The optimization is represented by the implementation of novel pre- and post-processing strategies that aim to mitigate the side effects introduced by different acquisition conditions affecting change detection analysis. Special modules have been designed in order to decrease the required memory when large data sets are processed.
引用
收藏
页码：1505 / 1526
页数：22
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