# Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images

arXiv: https://arxiv.org/abs/2009.02130 · HTML: https://mlsanity.com/paper/2009.02130/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 30 of 59)

- [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)
- [LSKNet: A Foundation Lightweight Backbone for Remote Sensing](https://mlsanity.com/paper/2403.11735/) (2024)
- [Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images](https://mlsanity.com/paper/2106.12413/) (2021)
- [EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2403.01482/) (2024)
- [Graph Information Bottleneck for Remote Sensing Segmentation](https://mlsanity.com/paper/2312.02545/) (2023)
- [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)
- [Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation](https://mlsanity.com/paper/2205.11319/) (2022)
- [Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2404.04531/) (2024)
- [PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery](https://mlsanity.com/paper/2406.10828/) (2024)
- [SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image](https://mlsanity.com/paper/2212.07623/) (2022)
- [Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2504.02647/) (2025)
- [LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery](https://mlsanity.com/paper/2406.16502/) (2024)
- [FSDENet: A Frequency and Spatial Domains based Detail Enhancement Network for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2510.00059/) (2025)
- [LSwinSR: UAV Imagery Super-Resolution based on Linear Swin Transformer](https://mlsanity.com/paper/2303.10232/) (2023)
- [MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2312.12735/) (2023)
- [Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion](https://mlsanity.com/paper/2410.05624/) (2024)
- [Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images](https://mlsanity.com/paper/2501.14413/) (2025)
- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images](https://mlsanity.com/paper/2103.07935/) (2021)
- [UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2408.11545/) (2024)
- [TT3D: Table Tennis 3D Reconstruction](https://mlsanity.com/paper/2504.10035/) (2025)
- [Globally Scalable Glacier Mapping by Deep Learning Matches Expert Delineation Accuracy](https://mlsanity.com/paper/2401.15113/) (2024)
- [PPMamba: A Pyramid Pooling Local Auxiliary SSM-Based Model for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2409.06309/) (2024)
- [Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection](https://mlsanity.com/paper/2510.10378/) (2025)
- [LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images](https://mlsanity.com/paper/2303.07747/) (2023)
- [Remote Sensing Semantic Segmentation Quality Assessment based on Vision Language Model](https://mlsanity.com/paper/2502.13990/) (2025)
- [LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images](https://mlsanity.com/paper/2212.08490/) (2022)
- [SACANet: scene-aware class attention network for semantic segmentation of remote sensing images](https://mlsanity.com/paper/2304.11424/) (2023)

## Abstract (arXiv)

Semantic segmentation of remote sensing images plays an important role in a wide range of applications including land resource management, biosphere monitoring and urban planning. Although the accuracy of semantic segmentation in remote sensing images has been increased significantly by deep convolutional neural networks, several limitations exist in standard models. First, for encoder-decoder architectures such as U-Net, the utilization of multi-scale features causes the underuse of information, where low-level features and high-level features are concatenated directly without any refinement. Second, long-range dependencies of feature maps are insufficiently explored, resulting in sub-optimal feature representations associated with each semantic class. Third, even though the dot-product attention mechanism has been introduced and utilized in semantic segmentation to model long-range dependencies, the large time and space demands of attention impede the actual usage of attention in application scenarios with large-scale input. This paper proposed a Multi-Attention-Network (MANet) to address these issues by extracting contextual dependencies through multiple efficient attention modules. A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention. Based on kernel attention and channel attention, we integrate local feature maps extracted by ResNeXt-101 with their corresponding global dependencies and reweight interdependent channel maps adaptively. Numerical experiments on three large-scale fine resolution remote sensing images captured by different satellite sensors demonstrate the superior performance of the proposed MANet, outperforming the DeepLab V3+, PSPNet, FastFCN, DANet, OCRNet, and other benchmark approaches.