# Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1812.05815 · HTML: https://mlsanity.com/paper/1812.05815/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2018.

## Built on by (most cited first, 11 of 11)

- [Unsupervised Change Detection Based on Image Reconstruction Loss](https://mlsanity.com/paper/2204.01200/) (2022)
- [Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model](https://mlsanity.com/paper/2406.08020/) (2024)
- [UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation](https://mlsanity.com/paper/2308.01146/) (2023)
- [Deep Metric Learning for Unsupervised Remote Sensing Change Detection](https://mlsanity.com/paper/2303.09536/) (2023)
- [Adaptive Bernstein Change Detector for High-Dimensional Data Streams](https://mlsanity.com/paper/2306.12974/) (2023)
- [Updating Street Maps using Changes Detected in Satellite Imagery](https://mlsanity.com/paper/2110.06456/) (2021)
- [Unsupervised Change Detection of Extreme Events Using ML On-Board](https://mlsanity.com/paper/2111.02995/) (2021)
- [SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection](https://mlsanity.com/paper/2404.11326/) (2024)
- [ML Updates for OpenStreetMap: Analysis of Research Gaps and Future Directions](https://mlsanity.com/paper/2407.03365/) (2024)
- [MM811 Project Report: Cloud Detection and Removal in Satellite Images](https://mlsanity.com/paper/2212.11369/) (2022)
- [Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster](https://mlsanity.com/paper/2509.02440/) (2025)

## Abstract (arXiv)

This paper proposes an efficient unsupervised method for detecting relevant changes between two temporally different images of the same scene. A convolutional neural network (CNN) for semantic segmentation is implemented to extract compressed image features, as well as to classify the detected changes into the correct semantic classes. A difference image is created using the feature map information generated by the CNN, without explicitly training on target difference images. Thus, the proposed change detection method is unsupervised, and can be performed using any CNN model pre-trained for semantic segmentation.