The speed of the image clustering method based on the spectral clustering algorithm is greatly affected by the size of image resolution; in many cases, the processing of high-resolution images cannot be completed precisely on time. Thus, based on the connected area labeling and superpixel spectral clustering algorithm, this paper proposes a fast change detection method for remote sensing images based on the connected area labeling method and spectral clustering algorithm. In the initial stage, the method adopts the idea of non-local (NL)-means algorithm to generate NL difference image, then, through the OTSU method, several critical areas with large connected areas are discriminated according to a threshold, and the rectangular contour internal images of these areas are extracted and processed by the superpixel spectral clustering algorithm, to realize the change detection of the areas quickly. The results of the application experiments and the performance experiments proved that the CASC method could solve the feasibility of spectral clustering algorithm to deal with high-resolution remote sensing images to a certain extent, has good robustness, and the processing speed has been dramatically improved. The proposed method can quickly and efficiently extract sensitive sub-areas with significant changes in the study area and provide basis and decision support for geological monitoring and research in critical areas in the future.