Abstract
One of the objectives of image processing is to detect the region of interest (ROI) in the given application, and then perform characterization and classification of these regions. In HyperSpectral Images (HSI) the detection of targets in an image is of great interest for several applications. Generally, when ROI containing targets is previously selected, the detection results are better. In this paper we propose to select the ROI with a new edge detection method for large HSI containing objects with large and small sizes, based on tensorial modeling, and an estimation of local rank variations.
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Fossati, C., Bourennane, S., Cailly, A. (2015). Edge Detection Method Based on Signal Subspace Dimension for Hyperspectral Images. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_69
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DOI: https://doi.org/10.1007/978-3-319-25903-1_69
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