Enhanced Lightweight End-to-End Semantic Segmentation for High-Resolution Remote Sensing Images

Deep learning based methods have shown promising performance in semantic segmentation of high-resolution remote sensing (HRRS) images. However, due to the multi-scale property and complexity of HRRS images, it still faces many challenges in tackling the scale variance problem and obtaining global context information. In this paper, we propose an enhanced lightweight end-to-end semantic segmentation (ELES2) framework for HRRS images, where a superpixel segmentation pooling (SSP) module is embedded with the framework for result refinement, leading to a more accurate end-to-end semantic segmentation. Besides, compensation connections (CC) are applied between encoder blocks to establish long-range dependencies. In addition, a dense dilated convolutional pyramid (DDCP) module is proposed to generate dense features under different scales and capture global context information. Experiments conducted showed that our ELES2 respectively achieves the mean pixel intersection-over-union (mIoU) values of 80.16% and 73.20% on the ISPRS Potsdam and Vaihingen benchmark datasets using only 12.62M parameters and 13.09G floating-point operations (FLOPs). Experimental results prove that our method achieves a promising balance between segmentation accuracy and computational efficiency compared with the state-of-the-art semantic segmentation models.