no code implementations • 31 Mar 2014 • Miguel Simões, José Bioucas-Dias, Luis B. Almeida, Jocelyn Chanussot
Hyperspectral remote sensing images (HSIs) are characterized by having a low spatial resolution and a high spectral resolution, whereas multispectral images (MSIs) are characterized by low spectral and high spatial resolutions.
no code implementations • 19 Sep 2014 • Qi Wei, José Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret
This paper presents a variational based approach to fusing hyperspectral and multispectral images.
no code implementations • 14 Nov 2014 • Miguel Simões, José Bioucas-Dias, Luis B. Almeida, Jocelyn Chanussot
Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution.
no code implementations • 7 Jul 2015 • Xiao Fu, Wing-Kin Ma, José Bioucas-Dias, Tsung-Han Chan
The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing.
no code implementations • 3 Sep 2015 • Filipe Condessa, José Bioucas-Dias, Carlos Castro, John Ozolek, Jelena Kovačević
We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information.
1 code implementation • 3 Feb 2016 • Miguel Simões, Luis B. Almeida, José Bioucas-Dias, Jocelyn Chanussot
In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast.
no code implementations • 11 Oct 2016 • João P. Oliveira, Ana Bragança, José Bioucas-Dias, Mário Figueiredo, Luís Alcácer, Jorge Morgado, Quirina Ferreira
In this article, we present a denoising algorithm to improve the interpretation and quality of scanning tunneling microscopy (STM) images.
3 code implementations • 12 Mar 2018 • Charis Lanaras, José Bioucas-Dias, Silvano Galliani, Emmanuel Baltsavias, Konrad Schindler
The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution.
no code implementations • 12 Jun 2018 • Miguel Simões, José Bioucas-Dias, Luis B. Almeida
Many of the algorithms used to solve minimization problems with sparsity-inducing regularizers are generic in the sense that they do not take into account the sparsity of the solution in any particular way.
no code implementations • 7 Feb 2019 • Adrien Lagrange, Mathieu Fauvel, Stéphane May, José Bioucas-Dias, Nicolas Dobigeon
The attribution vectors of the clustering are then used as features vectors for the classification task, i. e., the coding vectors of the corresponding factorization problem.