Context-Aware Feature Enhancement Network for Remote Sensing Image Semantic Segmentation

Semantic segmentation of remote sensing images plays a crucial role in accurate land-cover classification and environmental monitoring. However, existing semantic segmentation networks still struggle with multiscale feature extraction and context modeling. To address these challenges, this paper proposes a novel semantic segmentation network, termed Context-aware Feature Enhancement Network (CFENet). Specifically, we design a Wavelet-Based Pyramid Pooling Module (WPPM) based on Haar wavelet downsampling (HWD) to enhance the model’s ability to extract multiscale features. Meanwhile, an Adaptive Context Enhancement (ACE) module is introduced to adaptively focus on semantically significant regions, enabling joint enhancement along both spatial and channel dimensions. In addition, we develop a Multiscale Feature Reconstruction (MFR) module that performs multiscale decoding on the output of ACE in the decoding stage to further improve segmentation accuracy. The effectiveness of CFENet is validated on two benchmark datasets: ISPRS Vaihingen and ISPRS Potsdam. Experimental results show that, compared to baseline models, CFENet improves the mF1 by 3.16% and 2.38%, the OA by 1.54% and 2.80%, and the mIoU by 4.44% and 3.91%, respectively. Moreover, CFENet achieves reliable and satisfactory performance when evaluated against several representative mainstream methods.