Unsupervised Change Detection on Multi-Temporal Satellite Images Using Tensor Decomposition Learning

The substantial volume of continuously gathered remote sensing data can serve as a valuable information source in mitigating the effects of natural disasters. This involves identifying changes in the time series of observations. Considering that the precise location of the changes may not be available in real-world scenarios, we propose an unsupervised method for detecting extreme events in multi-temporal satellite images. Specifically, we learn a basis matrix of each dimension of the feature space of the images using the tensor decomposition learning method. Then, each new image is represented in the feature space by expressing it as a multilinear combination of the learned tensor decomposition factors. The predicted changes can be obtained by comparing and thresholding the distance of the corresponding extracted features of the images before and after the event. Experimental results on real Sentinel-2 multi-temporal images demonstrate that the proposed method can efficiently detect the effects of fires and floods with low complexity.@INPROCEEDINGS{10642883, author={Aidini, Anastasia and Tsagkatakis, Grigorios and Tsakalides, Panagiotis}, booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium}, title={Unsupervised Change Detection on Multi-Temporal Satellite Images Using Tensor Decomposition Learning}, year={2024}, volume={}, number={}, pages={8495-8499}, keywords={Learning systems;Tensors;Disasters;Time series analysis;Feature extraction;Satellite images;Complexity theory;Unsupervised change detection;Tensor decomposition learning;Multi-temporal data;Feature space}, doi={10.1109/IGARSS53475.2024.10642883}}