Superpixel segmentation of high-resolution remote sensing image based on feature reconstruction method by salient edges

Superpixel segmentation is an important image preprocessing technology that can aggregate adjacent pixels with similar characteristics to reduce the complexity of subsequent tasks. However, with the increasing spatial resolution of remote sensing images, maintaining accurate boundary information of ground objects during superpixel segmentation has become a challenge. We propose a feature reconstruction method based on edge detection and adaptive morphological reconstruction to enhance feature representation for superpixel segmentation. Specifically, we employ the structured forest model to extract robust edges from the image, and an adaptive morphological reconstruction method is proposed to eliminate noise from the edge information. Furthermore, a feature fusion method based on wavelet transform is constructed to integrate the extracted edge information with the original image, which further enhances the segmentation accuracy of image edges in superpixel segmentation. Experimental results on three datasets of natural and remote sensing images demonstrate that the proposed method can effectively construct the feature representation of images and achieve better superpixel segmentation results than other methods.