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To fully utilize spatial information for segmentation and address the challenge of handling areas with significant grayscale variations in remote sensing segmentation, we propose the spatial and frequency domain fusion network (SFFNet) framework. This framework employs a two-stage network design: the first stage extracts features using spatial methods to obtain features with sufficient spatial details and semantic information; the second stage maps these features in both spatial and frequency domains. In the frequency domain mapping, we introduce the wavelet transform feature decomposer (WTFD) structure, which decomposes features into low-frequency and high-frequency components using the Haar wavelet transform and integrates them with spatial features. To bridge the semantic gap between frequency and spatial features, facilitating significant feature selection to promote the combination of features from different representation domains, we design the multiscale dual-representation alignment filter (MDAF). This structure utilizes multiscale convolutions and dual-cross attentions. Comprehensive experimental results demonstrate that, compared to existing methods, SFFNet achieves superior performance in terms of mean intersection over union (mIoU), reaching 84.80% and 87.73%, respectively. The code is located at https://github.com/yysdck/SFFNet.
Yang et al. (Mon,) studied this question.