Journal Article

A Spectral–Spatial Context-Boosted Network for Semantic Segmentation of Remote Sensing Images

Xu, Zhennan, Fang, Yiwei, Li, Tao, Yong, Xi, Wang, Xinyuan, Tong, Yao, You, Qian, Li, Xin, Gao, Hongmin, Lyu, Xin

2024

Overview

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in F1-scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model’s performance, emphasizing the effectiveness of these components in capturing detailed contextual cues.

• Publisher: MDPI AG
• Subject: Ablation / Accuracy / Artificial intelligence / Classification / Comparative analysis / Context / cross-fusion module / data collection / Design / Image processing / Image segmentation / Machine learning / Methods / natural resources conservation / Neural networks / Remote sensing / remote sensing images / Satellites / Semantic segmentation / Semantics / Spatial data / Spatial discrimination learning / spectral–spatial context / synergetic attention / Technology application / Urban agriculture / Urban planning