Ground collapse was a typical geological disaster in karstic area. Comparing to other geological disaster， ground collapses were considerably small in scale and dispersive in distribution. This made detecting and identification of ground collapse in urban areas quite a challenging work. In this paper， an object-based image analysis method was used to detect the ground collapse sites using remote sensing images. Firstly， multi-scale image segmentation was performed on the 0.2 meter aerial image of study area and over tens of spatial， spectral， shape and texture features were extracted based on the segmented image objects. Then eight optimized features for ground collapse classification was selected using generic algorithm(GA)， which obtains the best fitness value in ground collapse classification. After that， some on the spot ground collapses were used as cases sites and cased-based-reasoning(CBR) classification was applied on all the segmented image objects， from large scale to small scale. In the end， classification accuracy was evaluated over the whole study area. The overall object-based CBR classification of ground collapse area is about 0.881 and the kappa coefficient is 0.791. Higher accuracy(0.889) is achieved for the ripe ground collapses detection. The same case library was also applied to another trial area for reusability testing and achieved satisfactory results. In conclusion， CBR method could be successfully applied to ground collapses detection using high resolution images. CBR method proposed in this paper could achieve betters classification accuracy than traditional supervised classification methods.