# Self-supervised Change Detection in Multi-view Remote Sensing Images

arXiv: https://arxiv.org/abs/2103.05969 · HTML: https://mlsanity.com/paper/2103.05969/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2021.

## Built on by (most cited first, 25 of 25)

- [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)
- [Self-Supervised Multimodal Learning: A Survey](https://mlsanity.com/paper/2304.01008/) (2023)
- [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022)
- [FCCDN: Feature Constraint Network for VHR Image Change Detection](https://mlsanity.com/paper/2105.10860/) (2021)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation](https://mlsanity.com/paper/2205.11319/) (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)
- [CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding](https://mlsanity.com/paper/2310.00022/) (2023)
- [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025)
- [Graph Signal Processing for Heterogeneous Change Detection Part I: Vertex Domain Filtering](https://mlsanity.com/paper/2208.01881/) (2022)
- [Deep Metric Learning for Unsupervised Remote Sensing Change Detection](https://mlsanity.com/paper/2303.09536/) (2023)
- [Unsupervised CD in satellite image time series by contrastive learning and feature tracking](https://mlsanity.com/paper/2304.11375/) (2023)
- [Incomplete Multimodal Learning for Remote Sensing Data Fusion](https://mlsanity.com/paper/2304.11381/) (2023)
- [Cross-sensor self-supervised training and alignment for remote sensing](https://mlsanity.com/paper/2405.09922/) (2024)
- [S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Images](https://mlsanity.com/paper/2502.12604/) (2025)
- [Self-supervised learning unveils change in urban housing from street-level images](https://mlsanity.com/paper/2309.11354/) (2023)
- [SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection](https://mlsanity.com/paper/2404.11326/) (2024)
- [Graph Signal Processing for Heterogeneous Change Detection Part II: Spectral Domain Analysis](https://mlsanity.com/paper/2208.01905/) (2022)
- [SwinV2DNet: Pyramid and Self-Supervision Compounded Feature Learning for Remote Sensing Images Change Detection](https://mlsanity.com/paper/2308.11159/) (2023)
- [Consistency Change Detection Framework for Unsupervised Remote Sensing Change Detection](https://mlsanity.com/paper/2511.08904/) (2025)
- [Spatially Selective Self-Training for Unsupervised Building Change Detection](https://mlsanity.com/paper/2606.10775/) (2026)

## Abstract (arXiv)

The vast amount of unlabeled multi-temporal and multi-sensor remote sensing data acquired by the many Earth Observation satellites present a challenge for change detection. Recently, many generative model-based methods have been proposed for remote sensing image change detection on such unlabeled data. However, the high diversities in the learned features weaken the discrimination of the relevant change indicators in unsupervised change detection tasks. Moreover, these methods lack research on massive archived images. In this work, a self-supervised change detection approach based on an unlabeled multi-view setting is proposed to overcome this limitation. This is achieved by the use of a multi-view contrastive loss and an implicit contrastive strategy in the feature alignment between multi-view images. In this approach, a pseudo-Siamese network is trained to regress the output between its two branches pre-trained in a contrastive way on a large dataset of multi-temporal homogeneous or heterogeneous image patches. Finally, the feature distance between the outputs of the two branches is used to define a change measure, which can be analyzed by thresholding to get the final binary change map. Experiments are carried out on five homogeneous and heterogeneous remote sensing image datasets. The proposed SSL approach is compared with other supervised and unsupervised state-of-the-art change detection methods. Results demonstrate both improvements over state-of-the-art unsupervised methods and that the proposed SSL approach narrows the gap between unsupervised and supervised change detection.