dc.contributor.author | Marcos Ramirez, Juan | |
dc.contributor.author | Martínez Torre, José Ignacio | |
dc.contributor.author | Arguello, Henry | |
dc.date.accessioned | 2020-12-10T10:58:18Z | |
dc.date.available | 2020-12-10T10:58:18Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Signal Processing: Image Communication Volume 90, January 2021, 116014 | es |
dc.identifier.issn | 0923-5965 | |
dc.identifier.uri | http://hdl.handle.net/10115/17310 | |
dc.description.abstract | In the compressive spectral imaging (CSI) framework, di erent architectures have been proposed to recover
high-resolution spectral images from compressive measurements. Since CSI architectures compactly capture
the relevant information of the spectral image, various methods that extract classi cation features from
compressive samples have been recently proposed. However, these techniques require a feature extraction
procedure that reorders measurements using the information embedded in the coded aperture patterns. In
this paper, a method that fuses features directly from dual-resolution compressive measurements is proposed
for spectral image classi cation. More precisely, the fusion method is formulated as an inverse problem that
estimates high-spatial-resolution and low-dimensional feature bands from compressive measurements. To
this end, the decimation matrices that describe the compressive measurements as degraded versions of the
fused features are mathematically modeled using the information embedded in the coded aperture patterns.
Furthermore, we include both a sparsity-promoting and a total-variation (TV) regularization terms to the
fusion problem in order to consider the correlations between neighbor pixels, and therefore, improve the
accuracy of pixel-based classi ers. To solve the fusion problem, we describe an algorithm based on the
accelerated variant of the alternating direction method of multipliers (accelerated-ADMM). Additionally,
a classi cation approach that includes the developed fusion method and a multilayer neural network is
introduced. Finally, the proposed approach is evaluated on three remote sensing spectral images and a set
of compressive measurements captured in the laboratory. Extensive simulations show that the proposed
classi cation approach outperforms other approaches under various performance metrics. | es |
dc.description.sponsorship | This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754382, GOT ENERGY TALENT. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Compressive spectral imaging | es |
dc.subject | Dual-resolution acquisition systems | es |
dc.subject | Feature fusion | es |
dc.subject | Spectral image classi cation | es |
dc.subject | Informática | es |
dc.title | Feature Fusion Via Dual-Resolution Compressive Measurement Matrix Analysis For Spectral Image Classi cation | es |
dc.type | info:eu-repo/semantics/article | es |
dc.identifier.doi | 10.1016/j.image.2020.116014 | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.relation.projectID | 754382, GOT ENERGY TALENT | es |
dc.subject.unesco | 1203.17 Informática | es |