Abstract
Hyperspectral sensing, due to its intrinsic ability to capture the spectral responses of depicted materials, provides unique capabilities towards object detection and identification. In this paper, we tackle the problem of man-made object detection from hyperspectral data through a deep learning classification framework. By the effective exploitation of a Convolutional Neural Network we encode pixels’ spectral and spatial information and employ a Multi-Layer Perceptron to conduct the classification task. Experimental results and the performed quantitative validation on widely used hyperspectral datasets demonstrating the great potentials of the developed approach towards accurate and automated man-made object detection.
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This research was funded from European Unions FP7 under grant agreement n.313161, eVACUATE Project (www.evacuate.eu)
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Makantasis, K., Karantzalos, K., Doulamis, A., Loupos, K. (2015). Deep Learning-Based Man-Made Object Detection from Hyperspectral Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_64
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DOI: https://doi.org/10.1007/978-3-319-27857-5_64
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