A Hybrid Metaheuristic and Deep Learning Approach for Change Detection in Remote Sensing Data


Volume: 12 | Issue: 5 | Pages: 9351-9356 | October 2022 | https://doi.org/10.48084/etasr.5246


This study aimed to adapt Convolutional Neural Networks (CNN) to solve the problem of change detection using remote sensing imagery. Specifically, the goal was to investigate the impact of each CNN layer to detect changes between two satellite images acquired on two different dates. As low-level CNN layers detect fine details (small changes) and higher-level layers detect coarse details (large changes), the idea was to assign a weight to each layer and use a genetic algorithm based on a training dataset to generalize the detection process on the test dataset. The results showed the effectiveness of the proposed approach based on two real-life datasets.


Change detection, Remote sensing, Deep learning, Convolutional neural networks, Genetic Algorithms


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How to Cite

Y. Slimani and R. Hedjam, “A Hybrid Metaheuristic and Deep Learning Approach for Change Detection in Remote Sensing Data”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 5, pp. 9351–9356, Oct. 2022.


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