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Communication Dans Un Congrès Année : 2018

A Study on the Sensitivity-Based Discriminative Hyperspectral Image Content Representation

Résumé

In this paper, we study the importance of spectral sensitivity functions in constructing discriminative representation of hyperspectral images (HSI). The main goal of a such representation is to improve image content recognition by focusing the processing only on the most relevant spectral channels. The underlying hypothesis is that for a given category, each image content is better extracted through a specific set of spectral sensitivity functions. In this study, we fixed the number of spectral sensitivity functions to 3 for displaying purposes. Deep features are then extracted from the obtained trichromatic representation of HSI data to build a discriminative image signature. Finally, spectral sensitivity functions are compared in a Content-Based Image Retrieval (CBIR) paradigm. Exhaustive experiments have been conducted on a hyperspectral dataset. Obtained results show the usefulness of the whole spectrum to obtain a discriminative image representation compared to the RGB representation.
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Dates et versions

hal-01912373 , version 1 (05-11-2018)

Identifiants

  • HAL Id : hal-01912373 , version 1

Citer

Olfa Ben Ahmed, Thierry Urruty, Noël Richard, Fernandez-Maloigne Christine. A Study on the Sensitivity-Based Discriminative Hyperspectral Image Content Representation. 16 th International Conference on Content -Based Multimedia Indexing, IEEE, 2018, La Rochelle, France. ⟨hal-01912373⟩
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