Rough set theory based object-oriented classification of high resolution remotely sensed imagery
粗糙集高分辨率遥感影像面向对象分类

Object-oriented classification has been paid more attention in the field of remote sensing. In this paper, a novel object-oriented algorithm based on rough set theory is proposed to classify different objects extracted from high-resolution remotely sensed imagery. The method consists of three steps. Firstly, image segmentation is achieved by watershed transform based on phase congruency gradient and foreground marking to extract image objects. Secondly, texture vector of each object is obtained by Gabor wavelet, and clustering rules is further formed based on the knowledge reduction theory. Finally, according to the restriction of the preliminary clustering result derived from spectral feature of objects, the ultimate classification is achieved referring to the rules. Meanwhile, a new technique to discretize continuous interval-valued attributes is developed, which is very suitable for the object-oriented classification, because the rough set is inadequate for dealing with continuous attributes. The experiments demonstrate that the proposed method can achieve better results and better accuracies.