Fuzzy Image Regions for Quantitative Land Cover Analysis
نویسندگان
چکیده
Fuzzy Image Regions were proposed recently as an alternative GEOBIA method for conducting qualitative land cover classification. In this paper, these fuzzy segments are applied for estimation of quantitative (i.e. compositional) land cover. The method comprises three main stages: (i) fuzzy segmentation to create segments with indeterminate boundaries and uncertain thematic allocation; (ii) feature analysis to evaluate contextual properties of fuzzy image regions; and (iii) final regression to estimate compositional land cover. The method is implemented using advanced machine learning techniques and tested in a rapidly urbanizing area using Landsat multispectral imagery. Experimental results suggest that the method produces accurate sub-pixel characterization of land cover classes. Thus, the proposed method is potentially useful for bridging the gap between the traditional quantitative and qualitative perspectives of remote sensing image analysis.
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