# StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization

arXiv: https://arxiv.org/abs/2004.06402 · HTML: https://mlsanity.com/paper/2004.06402/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 23 of 23)

- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation](https://mlsanity.com/paper/2302.02561/) (2023)
- [Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey](https://mlsanity.com/paper/2112.03241/) (2021)
- [Universal Domain Adaptation for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2301.11387/) (2023)
- [Secure Domain Adaptation with Multiple Sources](https://mlsanity.com/paper/2106.12124/) (2021)
- [GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates](https://mlsanity.com/paper/2304.07750/) (2023)
- [SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery](https://mlsanity.com/paper/2406.18151/) (2024)
- [Source-Free Domain Adaptation for YOLO Object Detection](https://mlsanity.com/paper/2409.16538/) (2024)
- [Multi-source Domain Adaptation for Panoramic Semantic Segmentation](https://mlsanity.com/paper/2408.16469/) (2024)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)
- [Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation](https://mlsanity.com/paper/2204.02111/) (2022)
- [Taxonomy-Structured Domain Adaptation](https://mlsanity.com/paper/2306.07874/) (2023)
- [Learning De-Biased Representations for Remote-Sensing Imagery](https://mlsanity.com/paper/2410.04546/) (2024)
- [Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner](https://mlsanity.com/paper/2105.14693/) (2021)
- [CoDEx: Combining Domain Expertise for Spatial Generalization in Satellite Image Analysis](https://mlsanity.com/paper/2504.19737/) (2025)
- [Source-Free Online Domain Adaptive Semantic Segmentation of Satellite Images under Image Degradation](https://mlsanity.com/paper/2401.02113/) (2024)
- [Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection](https://mlsanity.com/paper/2202.05941/) (2022)
- [Self-supervised Domain-agnostic Domain Adaptation for Satellite Images](https://mlsanity.com/paper/2309.11109/) (2023)
- [Unsupervised Federated Domain Adaptation for Segmentation of MRI Images](https://mlsanity.com/paper/2401.02941/) (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)
- [Domain Adaptation for Big Data in Agricultural Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.05972/) (2025)
- [VPRef: A Cross-Domain Benchmark for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2609.16486/) (2026)

## Abstract (arXiv)

Domain adaptation for semantic segmentation has recently been actively studied to increase the generalization capabilities of deep learning models. The vast majority of the domain adaptation methods tackle single-source case, where the model trained on a single source domain is adapted to a target domain. However, these methods have limited practical real world applications, since usually one has multiple source domains with different data distributions. In this work, we deal with the multi-source domain adaptation problem. Our method, namely StandardGAN, standardizes each source and target domains so that all the data have similar data distributions. We then use the standardized source domains to train a classifier and segment the standardized target domain. We conduct extensive experiments on two remote sensing data sets, in which the first one consists of multiple cities from a single country, and the other one contains multiple cities from different countries. Our experimental results show that the standardized data generated by StandardGAN allow the classifiers to generate significantly better segmentation.