# Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images

arXiv: https://arxiv.org/abs/2011.14302 · HTML: https://mlsanity.com/paper/2011.14302/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 26 of 26)

- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2104.12137/) (2021)
- [Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images](https://mlsanity.com/paper/2106.12413/) (2021)
- [ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2102.02531/) (2021)
- [Building extraction with vision transformer](https://mlsanity.com/paper/2111.15637/) (2021)
- [SFFNet: A Wavelet-Based Spatial and Frequency Domain Fusion Network for Remote Sensing Segmentation](https://mlsanity.com/paper/2405.01992/) (2024)
- [A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.11160/) (2024)
- [Open-Vocabulary Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2409.07683/) (2024)
- [Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation](https://mlsanity.com/paper/2508.04022/) (2025)
- [Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2504.02647/) (2025)
- [Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation](https://mlsanity.com/paper/2303.08401/) (2023)
- [LSwinSR: UAV Imagery Super-Resolution based on Linear Swin Transformer](https://mlsanity.com/paper/2303.10232/) (2023)
- [Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion](https://mlsanity.com/paper/2410.05624/) (2024)
- [Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images](https://mlsanity.com/paper/2103.07935/) (2021)
- [AeroReformer: Aerial Referring Transformer for UAV-based Referring Image Segmentation](https://mlsanity.com/paper/2502.16680/) (2025)
- [Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation](https://mlsanity.com/paper/2602.10137/) (2026)
- [Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing Segmentation](https://mlsanity.com/paper/2405.13686/) (2024)
- [Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers](https://mlsanity.com/paper/2206.09731/) (2022)
- [A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images](https://mlsanity.com/paper/2102.07997/) (2021)
- [DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation](https://mlsanity.com/paper/2305.19787/) (2023)
- [AMMUNet: Multi-Scale Attention Map Merging for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2404.13408/) (2024)
- [From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2505.15147/) (2025)
- [EMRA-proxy: Enhancing Multi-Class Region Semantic Segmentation in Remote Sensing Images with Attention Proxy](https://mlsanity.com/paper/2505.17665/) (2025)
- [AMMNet: An Asymmetric Multi-Modal Network for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2507.16158/) (2025)
- [Parameter-Efficient Modality-Balanced Symmetric Fusion for Multimodal Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2603.17705/) (2026)
- [MPerS: Dynamic MLLM MixExperts Perception-Guided Remote Sensing Scene Segmentation](https://mlsanity.com/paper/2605.10769/) (2026)

## Abstract (arXiv)

The attention mechanism can refine the extracted feature maps and boost the classification performance of the deep network, which has become an essential technique in computer vision and natural language processing. However, the memory and computational costs of the dot-product attention mechanism increase quadratically with the spatio-temporal size of the input. Such growth hinders the usage of attention mechanisms considerably in application scenarios with large-scale inputs. In this Letter, we propose a Linear Attention Mechanism (LAM) to address this issue, which is approximately equivalent to dot-product attention with computational efficiency. Such a design makes the incorporation between attention mechanisms and deep networks much more flexible and versatile. Based on the proposed LAM, we re-factor the skip connections in the raw U-Net and design a Multi-stage Attention ResU-Net (MAResU-Net) for semantic segmentation from fine-resolution remote sensing images. Experiments conducted on the Vaihingen dataset demonstrated the effectiveness and efficiency of our MAResU-Net. Open-source code is available at https://github.com/lironui/Multistage-Attention-ResU-Net.