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Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks

Convolutional NN for change detection

This project deals with the task of detecting relevant changes between two satellite images taken of the same scene at different times. A convolutional neural network (CNN) and semantic segmentation is implemented to detect the changes between the images, as well as classify the changes into the correct semantic class. A difference image is created using the feature maps generated by the CNN, which means that the CNN does not need to learn the non-linear mapping between two images and is thus unsupervised in the task of change detection.

Below is a diagram of the Unet architecture used in this project:

The research paper is available on ArXiV: https://arxiv.org/abs/1812.05815 The paper has been published in the proceedings of the IEEE International Joint Conference on Neural Networks, 2019