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Communication Dans Un Congrès Année : 2019

A Metrological Measurement of Texture in Hyperspectral Images Using Relocated Spectral Difference Occurrence Matrix

Résumé

A new hyperspectral texture descriptor, Relocated Spectral Difference Occurrence Matrix (rSDOM) is proposed. It assesses the distribution of spectral difference in a given neighborhood. For metrological purposes, rSDOM employs Kullback-Leibler pseudo-divergence (KLPD) for spectral difference calculation. It is generic and adapted for any spectral range and number of band. As validation, a texture classification scheme based on nearest neighbor classifier is applied on HyTexiLa dataset using rSDOM. The performance is close to Opponent Band Local Binary Pattern (OBLBP) with classification accuracy of 94.7%, but at a much-reduced feature size (0.24% of OBLBP's) and computational complexity.
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Dates et versions

hal-02481586 , version 1 (21-02-2020)

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Rui Jian Chu, Noël Richard, Christine Fernandez-Maloigne, Jon Yngve Hardeberg. A Metrological Measurement of Texture in Hyperspectral Images Using Relocated Spectral Difference Occurrence Matrix. 2019 IEEE International Conference on Image Processing (ICIP), Sep 2019, Taipei, Taiwan. pp.3133-3137, ⟨10.1109/ICIP.2019.8803378⟩. ⟨hal-02481586⟩
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