# SFFNet: A Wavelet-Based Spatial and Frequency Domain Fusion Network for Remote Sensing Segmentation

arXiv: https://arxiv.org/abs/2405.01992 · HTML: https://mlsanity.com/paper/2405.01992/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 61 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 25 of 25)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization](https://mlsanity.com/paper/2303.14189/) (2023)
- [Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models](https://mlsanity.com/paper/1807.05713/) (2018)
- [A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2104.12137/) (2021)
- [Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning](https://mlsanity.com/paper/1703.06452/) (2017)
- [Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models](https://mlsanity.com/paper/1803.06253/) (2018)
- [Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2011.14302/) (2020)
- [Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks](https://mlsanity.com/paper/2008.04021/) (2020)
- [Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2111.11057/) (2021)
- [Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network](https://mlsanity.com/paper/2008.00168/) (2020)
- [Seeing Text in the Dark: Algorithm and Benchmark](https://mlsanity.com/paper/2404.08965/) (2024)
- [DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation](https://mlsanity.com/paper/2301.01202/) (2022)

## Built on by (most cited first, 17 of 17)

- [Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2504.02647/) (2025)
- [FSDENet: A Frequency and Spatial Domains based Detail Enhancement Network for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2510.00059/) (2025)
- [Frequency-Compensated Network for Daily Arctic Sea Ice Concentration Prediction](https://mlsanity.com/paper/2504.16745/) (2025)
- [F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation](https://mlsanity.com/paper/2506.07847/) (2025)
- [Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters](https://mlsanity.com/paper/2504.10929/) (2025)
- [Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios](https://mlsanity.com/paper/2601.00537/) (2026)
- [Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans](https://mlsanity.com/paper/2502.14363/) (2025)
- [MTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization](https://mlsanity.com/paper/2506.17540/) (2025)
- [WD-FQDet: Multispectral Detection Transformer via Wavelet Decomposition and Frequency-aware Query Learning](https://mlsanity.com/paper/2605.13621/) (2026)
- [UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation](https://mlsanity.com/paper/2406.16129/) (2024)
- [Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction](https://mlsanity.com/paper/2505.12280/) (2025)
- [SSFMamba: Learning Symmetry-driven Spatial-Frequency Modeling for Physically Consistent 3D Medical Image Segmentation](https://mlsanity.com/paper/2508.03069/) (2025)
- [CF-Net: A Cross-Feature Reconstruction Network for High-Accuracy 1-Bit Target Classification](https://mlsanity.com/paper/2512.15105/) (2025)
- [CGFformer: Cluster-Guidance Frequency Transformer for Pansharpening](https://mlsanity.com/paper/2605.01490/) (2026)
- [TRNet: Learning with Topographic Priors for VHR Paddy Rice Mapping](https://mlsanity.com/paper/2608.04154/) (2026)
- [Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2608.15054/) (2026)
- [Hierarchical Adaptive Feature Refinement Network for VHR Remote Sensing Image Segmentation](https://mlsanity.com/paper/2608.15647/) (2026)

## Abstract (arXiv)

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.