# Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach

arXiv: https://arxiv.org/abs/2108.05094 · HTML: https://mlsanity.com/paper/2108.05094/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction](https://mlsanity.com/paper/1611.09842/) (2016)
- [Tile2Vec: Unsupervised representation learning for spatially distributed data](https://mlsanity.com/paper/1805.02855/) (2018)
- [A Deep and Tractable Density Estimator](https://mlsanity.com/paper/1310.1757/) (2013)
- [Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery](https://mlsanity.com/paper/1606.02585/) (2016)
- [Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks](https://mlsanity.com/paper/1609.06846/) (2016)
- [Visual search over billions of aerial and satellite images](https://mlsanity.com/paper/2002.02624/) (2020)

## Built on by (most cited first, 14 of 14)

- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [TerraMind: Large-Scale Generative Multimodality for Earth Observation](https://mlsanity.com/paper/2504.11171/) (2025)
- [CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding](https://mlsanity.com/paper/2304.09670/) (2023)
- [USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery](https://mlsanity.com/paper/2312.02199/) (2023)
- [Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2205.13769/) (2022)
- [Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery](https://mlsanity.com/paper/2409.19439/) (2024)
- [Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery](https://mlsanity.com/paper/2210.06786/) (2022)
- [Cross-sensor self-supervised training and alignment for remote sensing](https://mlsanity.com/paper/2405.09922/) (2024)
- [Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection](https://mlsanity.com/paper/2210.11815/) (2022)
- [SatSwinMAE: Efficient Autoencoding for Multiscale Time-series Satellite Imagery](https://mlsanity.com/paper/2405.02512/) (2024)
- [MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing](https://mlsanity.com/paper/2507.08683/) (2025)
- [Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic Labels](https://mlsanity.com/paper/2304.06857/) (2023)

## Abstract (arXiv)

In the application of machine learning to remote sensing, labeled data is often scarce or expensive, which impedes the training of powerful models like deep convolutional neural networks. Although unlabeled data is abundant, recent self-supervised learning approaches are ill-suited to the remote sensing domain. In addition, most remote sensing applications currently use only a small subset of the multi-sensor, multi-channel information available, motivating the need for fused multi-sensor representations. We propose a new self-supervised training objective, Contrastive Sensor Fusion, which exploits coterminous data from multiple sources to learn useful representations of every possible combination of those sources. This method uses information common across multiple sensors and bands by training a single model to produce a representation that remains similar when any subset of its input channels is used. Using a dataset of 47 million unlabeled coterminous image triplets, we train an encoder to produce semantically meaningful representations from any possible combination of channels from the input sensors. These representations outperform fully supervised ImageNet weights on a remote sensing classification task and improve as more sensors are fused. Our code is available at https://storage.cloud.google.com/public-published-datasets/csf_code.zip.