Abstract
This research focuses on feature-based, high-resolution multi-temporal image matching. The objective is to develop an efficient method for accurately matching features in images captured at different times. The proposed approach relies on the Visual Geometry Group-16 (VGG16) and VGG19 models for feature extraction and calculates Euclidean distances to enable accurate matching. A comparative analysis is carried out with Scale Invariant Feature Transform (SIFT) to evaluate the performance of both models. Experimental results demonstrate the effectiveness of the proposed approach in processing high-resolution multi-temporal imagery, highlighting the advantages of using the VGG19 and VGG16 models in feature-based matching techniques.
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El Bahi, O., Alaoui, A.O., Qaraai, Y., El Allaoui, A. (2024). Deep Feature-Based Matching of High-Resolution Multitemporal Images Using VGG16 and VGG19 Algorithms. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-031-48465-0_69
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DOI: https://doi.org/10.1007/978-3-031-48465-0_69
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