As the rapidly growing of availability of high-resolution urban SAR images,analysis of urban environments using SAR images has become an important task in the field of SAR image interpretation.Built-up areas are the dominant structures of urban environments.Detecting and analyzing built-up areas has attracted more and more attention of researchers interested in urban SAR image interpretation.In this paper we propose a method of detecting built-up areas from high-resolution SAR images using the GLCM(Gray-Level Cooccurrence Matrix) textural analysis.Our method is composed of two stages:initial localization of built-up areas and boundary adjustment.Both stages follow a flow of feature computation,Bhattacharyya-Distance-based feature selection and KNN(K-Nearest Neighbor) classification.The difference is that a block-by-block feature computation manner is used in the first stage while a pixel-by-pixel one is used in the second stage.Experiments are performed on images obtained by different SAR sensors.The results indicate that the best three or four features,which have the highest Bhattacharyya distance,lead to the high performance of initial localization,with detection rate higher than 80% and false alarm rate lower than 10%.With the boundary adjustment is implemented,the detected built-up-area boundaries gradually get close to the real boundaries.The experimental results of different SAR images show that the proposed method for built-up area detection is promising. As the rapidly growing of of high-resolution urban SAR images, analysis of urban environments using SAR images has become an important task in the field of SAR image interpretation.Built-up areas are the dominant structures of urban environments. Detection and analyzing built -up areas has attracted more and more attention of researchers interested in urban SAR image interpretation. this paper we propose a method of detecting built-up areas from high-resolution SAR images using the GLCM (Gray-Level Coherency Matrix) textural analysis. Our method is composed of two stages: initial localization of built-up areas and boundary adjustment. Both stages follow a flow of feature computation, Bhattacharyya-Distance-based feature selection and KNN (K-Nearest Neighbor) classification.The difference is that a block-by-block feature computation manner is used in the first stage while a pixel-by-pixel one is used in the second stage. Experiments are done on images obtained by different SAR sensors. The results indicate that the best three or four features, which have the highest Bhattacharyya distance, lead to the high performance of initial localization, with detection rate higher than 80% and false alarm rate lower than 10% .With the boundary adjustment is implemented, the detected built-up-area steps getting get close to the real boundary. the experimental results of different SAR images show that the proposed method for built-up area detection is promising.