Wetland Scene Segmentation of Remote Sensing Images Based on Lie Group Feature and Graph Cut Model

Given the increasingly severe destruction of wetlands in recent years, research and monitoring for wetland protection are urgently needed. However, wetland monitoring still faces significant challenges such as high manual costs and low image monitoring accuracy, primarily due to the complex composition of wetland terrain and diverse spectral characteristics, especially located in remote suburban areas. With their rich semantic and spectral features, studying wetlands as scenes in remote sensing images and segmenting them effectively can address these challenges. To address the issue of low segmentation accuracy on remote sensing image with GrabCut, this study proposes a remote sensing image segmentation extraction method, named SceneCut, based on feature extraction and multifeature joint graph cuts. The method consists of three parts: feature extraction, feature fusion, and multifeature joint graph cuts. In the first part, image features are calculated using a sliding window approach. In the second part, region covariance calculation and matrix eigenvalue decomposition are used to generate a multichannel feature image from the extracted feature vectors. Finally, in the third part, GrabCut is applied to the multichannel image to perform graph cuts, considering multiple features, and the segmentation result is mapped back to the original image to generate the segmented image. Experimental analysis and field exploration validation of wetland images conducted in this study demonstrate that compared to algorithms that only use RGB features for segmentation or those that do not consider the relationship between multiple features, SceneCut performs well in wetland scene extraction, especially in extracting boundaries with significant blending.