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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.
