Open Access

ARTICLE

CG-FCLNet: Category-Guided Feature Collaborative Learning Network for Semantic Segmentation of Remote Sensing Images

Min Yao^1,*, Guangjie Hu¹, Yaozu Zhang²

1 College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China
2 R&D Department, Shanghai Freesense Technology Co. Ltd., Shanghai, 200000, China

* Corresponding Author: Min Yao. Email:

Computers, Materials & Continua 2025, 83(2), 2751-2771. https://doi.org/10.32604/cmc.2025.060860

Received 12 November 2024; Accepted 24 February 2025; Issue published 16 April 2025

Abstract

Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing. Despite the success of Convolutional Neural Networks (CNNs), they often fail to capture inter-layer feature relationships and fully leverage contextual information, leading to the loss of important details. Additionally, due to significant intra-class variation and small inter-class differences in remote sensing images, CNNs may experience class confusion. To address these issues, we propose a novel Category-Guided Feature Collaborative Learning Network (CG-FCLNet), which enables fine-grained feature extraction and adaptive fusion. Specifically, we design a Feature Collaborative Learning Module (FCLM) to facilitate the tight interaction of multi-scale features. We also introduce a Scale-Aware Fusion Module (SAFM), which iteratively fuses features from different layers using a spatial attention mechanism, enabling deeper feature fusion. Furthermore, we design a Category-Guided Module (CGM) to extract category-aware information that guides feature fusion, ensuring that the fused features more accurately reflect the semantic information of each category, thereby improving detailed segmentation. The experimental results show that CG-FCLNet achieves a Mean Intersection over Union (mIoU) of 83.46%, an mF1 of 90.87%, and an Overall Accuracy (OA) of 91.34% on the Vaihingen dataset. On the Potsdam dataset, it achieves a mIoU of 86.54%, an mF1 of 92.65%, and an OA of 91.29%. These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.

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Keywords

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