Rita Ammanouil

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This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two(More)
This paper addresses the problem of blind fully-constrained linear unmixing of hyperspectral images. The endmembers and their cardinality are assumed unknown, but the endmember spectra are supposed to be present in the scene. Group Lasso regularization is used to extract the endmembers. The estimation problem is convex, and solved with the Alternating(More)
This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation. The proposed regularization relies upon the construction of a graph representation of the hyperspectral image. Each node in the graph represents a pixel's spectrum, and edges connect similar pixels. The proposed graph framework promotes smoothness in the(More)
As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN(More)
This communication introduces a new framework for incorporating spatial regularization into a nonlinear unmixing procedure dedicated to hyperspectral data. The proposed model promotes smooth spatial variations of the nonlinear component in the mixing model. The spatial regularizer and the nonlinear contributions are jointly modeled by a vector-valued(More)
Hyperspectral images are characterized by their large contiguous set of wavelengths. Therefore, it is possible to benefit from this `hyper' spectral information in order to reduce the classification and unmixing errors. For this reason, we propose new classification and unmixing techniques that take into account the correlation between successive spectral(More)
This communication proposes an unsupervised neighbor dependent nonlinear unmixing algorithm for hyperspectral data. The proposed mixing scheme models the reflectance vector of a pixel as the sum of a linear combination of the endmem-bers plus a nonlinear function acting on neighboring spectra. The nonlinear function belongs to a reproducing kernel Hilbert(More)
This paper presents a kernel-based nonlinear mixing model for hyperspectral data, where the nonlinear function belongs to a Hilbert space of vector valued functions. The proposed model extends the existing ones by accounting for band-dependent and neighboring nonlinear contributions. The key idea is to work under the assumption that nonlinear contributions(More)
This work proposes to solve the unmixing problem in a graph setting. The hyperspectral image is mapped to a weighted graph where every pixel spectrum is represented by a node, and similar nodes are connected by weighted edges. A graph-based Total Variation framework is incorporated within the unmixing problem. The graph topology allows to promote smoothness(More)
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