# Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagery

arXiv: https://arxiv.org/abs/2304.09874 · HTML: https://mlsanity.com/paper/2304.09874/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (2021)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Self-supervised representation learning from 12-lead ECG data](https://mlsanity.com/paper/2103.12676/) (2021)
- [Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding](https://mlsanity.com/paper/2104.07070/) (2021)
- [Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series](https://mlsanity.com/paper/2203.09270/) (2022)
- [Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale](https://mlsanity.com/paper/1704.02965/) (2017)
- [Land Use Classification in Remote Sensing Images by Convolutional Neural Networks](https://mlsanity.com/paper/1508.00092/) (2015)
- [Satellite Image Classification with Deep Learning](https://mlsanity.com/paper/2010.06497/) (2020)
- [Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery](https://mlsanity.com/paper/2007.02277/) (2020)
- [Deep learning with self-supervision and uncertainty regularization to count fish in underwater images](https://mlsanity.com/paper/2104.14964/) (2021)
- [Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study](https://mlsanity.com/paper/2110.02580/) (2021)
- [Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images](https://mlsanity.com/paper/2203.07707/) (2022)
- [Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks](https://mlsanity.com/paper/1706.07842/) (2017)
- [Weakly Supervised Semantic Segmentation of Satellite Images](https://mlsanity.com/paper/1904.03983/) (2019)
- [Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification](https://mlsanity.com/paper/2210.10633/) (2022)

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

- [Cross-Domain Transfer with Self-Supervised Spectral-Spatial Modeling for Hyperspectral Image Classification](https://mlsanity.com/paper/2601.18088/) (2026)

## Abstract (arXiv)

This work presents a novel domain adaption paradigm for studying contrastive self-supervised representation learning and knowledge transfer using remote sensing satellite data. Major state-of-the-art remote sensing visual domain efforts primarily focus on fully supervised learning approaches that rely entirely on human annotations. On the other hand, human annotations in remote sensing satellite imagery are always subject to limited quantity due to high costs and domain expertise, making transfer learning a viable alternative. The proposed approach investigates the knowledge transfer of selfsupervised representations across the distinct source and target data distributions in depth in the remote sensing data domain. In this arrangement, self-supervised contrastive learning-based pretraining is performed on the source dataset, and downstream tasks are performed on the target datasets in a round-robin fashion. Experiments are conducted on three publicly available datasets, UC Merced Landuse (UCMD), SIRI-WHU, and MLRSNet, for different downstream classification tasks versus label efficiency. In self-supervised knowledge transfer, the proposed approach achieves state-of-the-art performance with label efficiency labels and outperforms a fully supervised setting. A more in-depth qualitative examination reveals consistent evidence for explainable representation learning. The source code and trained models are published on GitHub.