# Self-Supervised Multisensor Change Detection

arXiv: https://arxiv.org/abs/2103.05102 · HTML: https://mlsanity.com/paper/2103.05102/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 66 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [A critical analysis of self-supervision, or what we can learn from a single image](https://mlsanity.com/paper/1904.13132/) (2019)
- [Learning to Cluster under Domain Shift](https://mlsanity.com/paper/2008.04646/) (2020)

## Built on by (most cited first, 19 of 19)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation](https://mlsanity.com/paper/2211.07044/) (2022)
- [Self-Supervised Multimodal Learning: A Survey](https://mlsanity.com/paper/2304.01008/) (2023)
- [CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding](https://mlsanity.com/paper/2304.09670/) (2023)
- [SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal](https://mlsanity.com/paper/2201.09613/) (2022)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [Multimodal contrastive learning for remote sensing tasks](https://mlsanity.com/paper/2209.02329/) (2022)
- [Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2205.13769/) (2022)
- [Self-supervised Remote Sensing Images Change Detection at Pixel-level](https://mlsanity.com/paper/2105.08501/) (2021)
- [HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection](https://mlsanity.com/paper/2207.09634/) (2022)
- [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025)
- [DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model](https://mlsanity.com/paper/2305.12410/) (2023)
- [Unsupervised Flood Detection on SAR Time Series](https://mlsanity.com/paper/2212.03675/) (2022)
- [Graph Signal Processing for Heterogeneous Change Detection Part I: Vertex Domain Filtering](https://mlsanity.com/paper/2208.01881/) (2022)
- [DC3DCD: unsupervised learning for multiclass 3D point cloud change detection](https://mlsanity.com/paper/2305.05421/) (2023)
- [GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2306.15868/) (2023)
- [Self-supervised learning unveils change in urban housing from street-level images](https://mlsanity.com/paper/2309.11354/) (2023)
- [Subimage Overlap Prediction: Task-Aligned Self-Supervised Pretraining For Semantic Segmentation In Remote Sensing Imagery](https://mlsanity.com/paper/2601.01781/) (2026)
- [JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering](https://mlsanity.com/paper/2606.31745/) (2026)

## Abstract (arXiv)

Most change detection methods assume that pre-change and post-change images are acquired by the same sensor. However, in many real-life scenarios, e.g., natural disaster, it is more practical to use the latest available images before and after the occurrence of incidence, which may be acquired using different sensors. In particular, we are interested in the combination of the images acquired by optical and Synthetic Aperture Radar (SAR) sensors. SAR images appear vastly different from the optical images even when capturing the same scene. Adding to this, change detection methods are often constrained to use only target image-pair, no labeled data, and no additional unlabeled data. Such constraints limit the scope of traditional supervised machine learning and unsupervised generative approaches for multi-sensor change detection. Recent rapid development of self-supervised learning methods has shown that some of them can even work with only few images. Motivated by this, in this work we propose a method for multi-sensor change detection using only the unlabeled target bi-temporal images that are used for training a network in self-supervised fashion by using deep clustering and contrastive learning. The proposed method is evaluated on four multi-modal bi-temporal scenes showing change and the benefits of our self-supervised approach are demonstrated.