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Library Median change vector analysis algorithm for land-use land-cover change detection from remote-sensing data

Median change vector analysis algorithm for land-use land-cover change detection from remote-sensing data

Median change vector analysis algorithm for land-use land-cover change detection from remote-sensing data

Resource information

Date of publication
December 2012
Resource Language
ISBN / Resource ID
AGRIS:US201600057282
Pages
605-614

Improved change vector analysis (ICVA) has recently been promoted as an effective algorithm for multi-class change detection. Unlike the conventional change vector analysis (CVA) that works on two-dimensional data, the ICVA works on multidimensional data. However, ICVA has limitations when the change vector is fraught with similar direction cosine values. In this article, a new algorithm, named median change vector analysis (MCVA) has been proposed for multi-class change detection. The algorithm is based on an enhanced 2n-dimensional feature space comprising direction cosine values of both the change vector and the median vector, which allows for more accurate detection of change classes than those obtained from ICVA. As a case study, the proposed algorithm has been implemented on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) images of a typical Indian city and surrounding areas for land-cover change detection.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Varshney, Avnish
Arora, Manoj Kumar
Ghosh, Jayanta Kumar

Publisher(s)
Data Provider