A lightweight self-supervised learning segmentation model for variable and complex high-resolution remote sensing images

Abstract

The complexity and variability of high-resolution remote sensing data, such as high intra-class variability and inter-class similarity, pose significant challenges to model segmentation. To address the problem, this paper constructs a lightweight self-supervised learning model for multi-label segmentation in the form of phased learning of multi-scale features. The model adopts axial depthwise separable convolutions to reduce computational complexity and enhance feature representation, utilizes dilated rates to capture large-scale and multi-scale contextual information for long-distance feature extraction, and incorporates convolution kernels of varying sizes to acquire both local and global feature information for the improved ability of learning feature representation. The experimental results show that our model achieves competitive performance and has smaller weight parameters, memory usage, and lower computational complexity compared with existing classical models that rely on large-scale weight parameters. Additionally, our ablation study delves into the encountered design issues to elucidate the rationality of our approach.