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A classical problem in hyperspectral imaging, referred to as hyperspectral unmixing, consists in estimating spectra associated with each material present in an image and their proportions in each pixel. In practice, illumination variations (e.g., due to declivity or complex interactions with the observed materials) and the possible presence of outliers can result in significant changes in both the...
In this paper, we propose a new model along with an algorithm for dictionary-based nonnegative matrix factorization. We show its effectiveness on spectral unmixing of hyperspectral images using self dictionary compared to state-of-the-art methods.
We propose a regularization function for hyperspectral image restoration based on a newly-designed structure tensor. We adopt a convex optimization approach with the use of the nuclear norm of a matrix, termed as spatio-spectral structure tensor. It consists of the gradient components of a hyperspectral image cube w.r.t. the spatio-spectral domain. The proposed approach allows to penalize variations...
In this paper a novel efficient online possibilistic clustering algorithm suitable for hyperspectral image clustering is proposed. The algorithm is an online version of the recently proposed adaptive possibilistic c-means (APCM) algorithm and inherits its basic advantage, that is the ability to adapt the involved parameters during its execution in order to track variations during the clustering formation...
Spectral unmixing (SU) is one of the most important and studied topics in hyperspectral image analysis. By means of spectral unmixing it is possible to decompose a hyperspectral image in its spectral components, the so-called endmembers, and their respective fractional spatial distributions, so-called abundance maps. The Canonical Polyadic (CP) tensor decomposition has proved to be a powerful tool...
Hyperspectral unmixing aims at determining the reference spectral signatures composing a hyperspectral image, their abundance fractions and their number. In practice, the spectral variability of the identified signatures induces significant abundance estimation errors. To address this issue, this paper introduces a new linear mixing model explicitly accounting for this phenomenon. In this setting,...
This paper proposes an unsupervised Bayesian algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability of the endmembers. Each image pixel is modeled as a linear combination of the end-members weighted by their corresponding abundances. Spatial endmember variability is introduced by considering the normal compositional model that assumes variable...
The profusion of spectral bands generated by the acquisition process of hyperspectral images generally leads to high computational costs. Such difficulties arise in particular with nonlinear unmixing methods, which are naturally more complex than linear ones. This complexity, associated with the high redundancy of information within the complete set of bands, make the search of band selection algorithms...
Collaborative-based representation classifiers have widely spread in the latest years achieving remarkable results in signal and image processing tasks. In this paper, we consider these approaches for the hyperspectral image classification. Specifically, we focus on collaborative and sparse representation classiiers and we perform an investigation on the role of the different regularizations and constraints...
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