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.