SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images

TL;DR

This paper introduces SCAttNet, a novel semantic segmentation network for high-resolution remote sensing images that uses spatial and channel attention modules to improve feature refinement and segmentation accuracy.

Contribution

The paper presents a new end-to-end network integrating lightweight spatial and channel attention modules specifically designed for HRRSIs.

Findings

Achieves better segmentation results on ISPRS Vaihingen and Potsdam datasets.

Outperforms several classic methods in semantic segmentation accuracy.

Demonstrates the effectiveness of attention modules in remote sensing image analysis.

Abstract

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.