  In order 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 SFFNet (Spatial and Frequency Domain
Fusion Network) 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,
and facilitate 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 mIoU, reaching 84.80% and 87.73% respectively.The code
is located at https://github.com/yysdck/SFFNet.
