Segmentation of hyperspectral images using self-organizing maps

Pawel Sanocki; Michal Kawulok; Bogdan Smolka; Jakub Nalepa

doi:10.1117/12.2586236

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12 April 2021 Segmentation of hyperspectral images using self-organizing maps

Pawel Sanocki, Michal Kawulok, Bogdan Smolka, Jakub Nalepa

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Proceedings Volume 11736, Real-Time Image Processing and Deep Learning 2021; 117360M (2021) https://doi.org/10.1117/12.2586236
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Hyperspectral image analysis has been attracting research attention in a variety of fields. Since the size of hyperspectral data cubes can easily reach gigabytes, their efficient transfer, manual delineation, and intrinsic heterogeneity have become serious obstacles in building ground-truth datasets in emerging scenarios. Therefore, applying supervised learners for the hyperspectral classification and segmentation remains a difficult yet very important task in practice, as segmentation is a pivotal step in the process of extracting useful information about the scanned area from such highly dimensional data. We tackle this problem using self-organizing maps and exploit an unsupervised algorithm for segmenting such imagery. The experimental study, performed over two benchmark hyperspectral scenes and backed up with the sensitivity analysis, showed that our technique can be applied for this purpose due to its flexibility, it delivers reliable segmentations, and offers fast operation.

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Pawel Sanocki, Michal Kawulok, Bogdan Smolka, and Jakub Nalepa "Segmentation of hyperspectral images using self-organizing maps", Proc. SPIE 11736, Real-Time Image Processing and Deep Learning 2021, 117360M (12 April 2021); https://doi.org/10.1117/12.2586236

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