# On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data

arXiv: https://arxiv.org/abs/2310.10490 · HTML: https://mlsanity.com/paper/2310.10490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation](https://mlsanity.com/paper/1809.09478/) (2018)
- [Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2101.10979/) (2021)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [High-Resolution Representations for Labeling Pixels and Regions](https://mlsanity.com/paper/1904.04514/) (2019)
- [Predicting with Confidence on Unseen Distributions](https://mlsanity.com/paper/2107.03315/) (2021)
- [Are Labels Always Necessary for Classifier Accuracy Evaluation?](https://mlsanity.com/paper/2007.02915/) (2020)
- [Using satellite imagery to understand and promote sustainable development](https://mlsanity.com/paper/2010.06988/) (2020)
- [What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?](https://mlsanity.com/paper/2106.05961/) (2021)
- [Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentation](https://mlsanity.com/paper/2205.00858/) (2022)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks](https://mlsanity.com/paper/1907.12859/) (2019)
- [DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images](https://mlsanity.com/paper/2005.06216/) (2020)
- [Automated 3D recovery from very high resolution multi-view satellite images](https://mlsanity.com/paper/1905.07475/) (2019)
- [Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery](https://mlsanity.com/paper/2012.04222/) (2020)

## Built on by (most cited first, 1 of 1)

- [Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions](https://mlsanity.com/paper/2312.06169/) (2023)

## Abstract (arXiv)

Recent deep learning-based methods outperform traditional learning methods on remote sensing (RS) semantic segmentation/classification tasks. However, they require large training datasets and are generally known for lack of transferability due to the highly disparate RS image content across different geographical regions. Yet, there is no comprehensive analysis of their transferability, i.e., to which extent a model trained on a source domain can be readily applicable to a target domain. Therefore, in this paper, we aim to investigate the raw transferability of traditional and deep learning (DL) models, as well as the effectiveness of domain adaptation (DA) approaches in enhancing the transferability of the DL models (adapted transferability). By utilizing four highly diverse RS datasets, we train six models with and without three DA approaches to analyze their transferability between these datasets quantitatively. Furthermore, we developed a straightforward method to quantify the transferability of a model using the spectral indices as a medium and have demonstrated its effectiveness in evaluating the model transferability at the target domain when the labels are unavailable. Our experiments yield several generally important yet not well-reported observations regarding the raw and adapted transferability. Moreover, our proposed label-free transferability assessment method is validated to be better than posterior model confidence. The findings can guide the future development of generalized RS learning models. The trained models are released under this link: https://github.com/GDAOSU/Transferability-Remote-Sensing