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Contributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques

Abstract : Hyperspectral images provided by modern spectrometers are composed of reflectance values at hundreds of narrow spectral bands covering a wide range of the electromagnetic spectrum. Since spectral reflectance differs for most of the materials or objects present in a given scene, hyperspectral image processing and analysis find many real-life applications. We address in this work the problem of unsupervised hyperspectral image segmentation following three distinct approaches. The first one is of Graph Embedding type and necessitates two steps : first, pixels of the original image patchs are compared using a spectral similarity measure and then objects obtained by local segmentations are fusioned by means of a similarity measure between objects. The second one is of Spectral Hashing or Semantic Hashing type. We first define a binary encoding of spectral variations and then propose a clustering segmentation relying on a k- mode classification algorithm adapted to the categorical nature of the data, the chosen distance being a generalized version of the classical Hamming distance. In the third one, we take advantage of the geometric information given by the manifolds associated to the images. Using the metric properties of the space of Riemannian metrics, that is the space of symmetric positive definite matrices, endowed with the so-called Fisher Rao metric, we propose a k-means algorithm to obtain a cluster partitioning of the image.
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Saadallah El Asmar. Contributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques. Traitement du signal et de l'image [eess.SP]. Université de La Rochelle, 2016. Français. ⟨NNT : 2016LAROS023⟩. ⟨tel-01661468⟩

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