Abstract
Abstract Spectral Change Vector Analysis (CVA) is based on multi-temporal images. In this paper, a dichotomy search which can be used on detecting changes in the threshold vector is adopted. Meanwhile, a supervised classification technique is used in the direction cosine space with the type of central point in the initial assay vector remote sensing images. Results are discussed in the last part of this paper, which show that CVA can extract change information effectively in our study area of Wuhan city.
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