Adaptive thematic object extraction from remote sensing image based on spectral matching

Abstract Thematic object extraction is of great significance to remote sensing applications. Its procedure is always complicated while the result is not so precise, especially for object with various subtypes. An adaptive extraction method based on spectral matching, considering both spectral and spatial information, is proposed to extract thematic object completely and accurately from remote sensing image. This method considers the essential spectral representation of thematic object through endmember selection, and then achieves complete extraction via “whole–local” scale spectral matching. Experiments on ETM+ images to extract water and bare land are employed, and the results demonstrate the effectiveness and universality of this method through comparison with maximum likelihood classification and support vector machine (SVM) classification.