# Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

arXiv: https://arxiv.org/abs/2011.09766 · HTML: https://mlsanity.com/paper/2011.09766/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2020.

## Built on by (most cited first, 30 of 73)

- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://mlsanity.com/paper/2209.08575/) (2022)
- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model](https://mlsanity.com/paper/2208.03987/) (2022)
- [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022)
- [The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot](https://mlsanity.com/paper/2306.16623/) (2023)
- [DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation](https://mlsanity.com/paper/2203.12560/) (2022)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining](https://mlsanity.com/paper/2403.13430/) (2024)
- [Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2108.07002/) (2021)
- [A Billion-scale Foundation Model for Remote Sensing Images](https://mlsanity.com/paper/2304.05215/) (2023)
- [SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery](https://mlsanity.com/paper/2209.13351/) (2022)
- [LSKNet: A Foundation Lightweight Backbone for Remote Sensing](https://mlsanity.com/paper/2403.11735/) (2024)
- [SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation](https://mlsanity.com/paper/2403.16605/) (2024)
- [SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints](https://mlsanity.com/paper/2312.02464/) (2023)
- [Directional Connectivity-based Segmentation of Medical Images](https://mlsanity.com/paper/2304.00145/) (2023)
- [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://mlsanity.com/paper/2103.06564/) (2021)
- [CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.22629/) (2024)
- [AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation](https://mlsanity.com/paper/2306.06842/) (2023)
- [Unsupervised Change Detection Based on Image Reconstruction Loss](https://mlsanity.com/paper/2204.01200/) (2022)
- [Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process](https://mlsanity.com/paper/2309.17031/) (2023)
- [BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images](https://mlsanity.com/paper/2105.07364/) (2021)
- [Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2212.10236/) (2022)
- [Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2412.19492/) (2024)
- [SG-BEV: Satellite-Guided BEV Fusion for Cross-View Semantic Segmentation](https://mlsanity.com/paper/2404.02638/) (2024)
- [A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.11160/) (2024)
- [Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2404.04531/) (2024)
- [Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery](https://mlsanity.com/paper/2106.00880/) (2021)
- [Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2310.12452/) (2023)
- [PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation](https://mlsanity.com/paper/2406.19632/) (2024)

## Abstract (arXiv)

Geospatial object segmentation, as a particular semantic segmentation task, always faces with larger-scale variation, larger intra-class variance of background, and foreground-background imbalance in the high spatial resolution (HSR) remote sensing imagery. However, general semantic segmentation methods mainly focus on scale variation in the natural scene, with inadequate consideration of the other two problems that usually happen in the large area earth observation scene. In this paper, we argue that the problems lie on the lack of foreground modeling and propose a foreground-aware relation network (FarSeg) from the perspectives of relation-based and optimization-based foreground modeling, to alleviate the above two problems. From perspective of relation, FarSeg enhances the discrimination of foreground features via foreground-correlated contexts associated by learning foreground-scene relation. Meanwhile, from perspective of optimization, a foreground-aware optimization is proposed to focus on foreground examples and hard examples of background during training for a balanced optimization. The experimental results obtained using a large scale dataset suggest that the proposed method is superior to the state-of-the-art general semantic segmentation methods and achieves a better trade-off between speed and accuracy. Code has been made available at: urlhttps://github.com/Z-Zheng/FarSeg.