# Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks

Source: [Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa65801e209131cea0a94d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:27:45.299Z

Authors: Kevin Louis de Jong, Anna Sergeevna Bosman

Published: 2018-12-14T08:15:15.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1812.05815>)

[PDF](<https://arxiv.org/pdf/1812.05815.pdf>)