Abstract: High-resolution remote sensing images have rich texture information, and pixel-level change detection methods mainly analyze the spectral information of the image. Therefore, the use of pixel-level change detection methods for high-resolution remote sensing images has certain limitations. In view of this, a high-resolution remote sensing image change detection method combining pixel level and object level is proposed to solve the problems of salt and pepper and misdetection in the pixel level and object level change detection methods. First, combining the multi-dimensional features of high-resolution remote sensing images, a remote sensing image change detection model is constructed. Secondly, the random forest classifier is used to classify the image, and the pixel-level change detection result is obtained. Finally, the pixel-level change detection result and the image object segmentation result are fused to obtain the image change area and the invariant area. Experimental results show that the algorithm has high accuracy and detection accuracy.

Key words: remote sensing image, change detection, random forest, pixel level, object level