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He is currently working toward the Ph. He is currently full professor in the school of systems engineering and computer science from the Universidad Industrial de Santander, Colombia. This means the journal and Pontificia Universidad Javeriana cannot be held responsible for any ethical malpractice by the authors. Publishing contents in this journal does not generate royalties for contributors. These images enable object and feature detection, classification, or identification based on their spectral characteristics.
An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture. Specifically, a sparsity-based target detection model that uses compressive measurement for the detection task is designed and tested using an optimization algorithm. Thus, this work may be reproduced, distributed, and publicly shared in digital format, as long as the names of the authors and Pontificia Universidad Javeriana are acknowledged. Hyperspectral imaging entails data typically spanning hundreds of contiguous wavebands in a certain spectral range.
Specifically, a sparsity-based target detection model that uses compressive measurement for the detection task is designed and tested using an optimization algorithm. Thus, this work may be reproduced, distributed, and publicly shared in digital format, as long as the names of the authors and Pontificia Universidad Javeriana are acknowledged. Keywords Compressive sensing; Hyperspectral target detection; Hyperspectral imaging; Sparsity model.
Approving the intervention of the work review, copy-editing, translation, layout and the following outreach, are granted through an use license and not through an assignment of rights. This work is registered under Creative Commons Attribution 4. Thus, this work may be reproduced, distributed, and publicly shared in digital format, as long as the names of the authors and Pontificia Universidad Javeriana are acknowledged. These images enable object and feature detection, classification, or identification based on their spectral characteristics.
Others are allowed to quote, adapt, transform, auto-archive, republish, and create based on this material, for any purpose even commercial onesprovided the authorship is duly acknowledged, a link to the original work is provided, and it is specified if changes have been made. Simulations show that it is possible to perform certain transformations to the dictionaries used in traditional target detection, in order to achieve an accurate image representation in the compressed subspace. He is currently working on his M. An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture. He is currently working toward the Ph.
As a consequence of the protection granted by the use license, the journal is not required to publish recantations or modify information already published, unless the errata stems from the editorial management process. This work is registered under Creative Commons Attribution 4. Novel architectures have been developed for the acquisition of compressive spectral images with just a few coded aperture focal plane array measurements.
An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture. User Username Password Remember me. He is currently working on his M.
This work is registered under Creative Commons Attribution 4. Keywords Compressive sensing; Hyperspectral target detection; Hyperspectral imaging; Sparsity model. He is currently working toward the Ph. Specifically, a sparsity-based target detection model that uses compressive measurement for the detection task is designed and tested using an optimization algorithm. This work focuses on the development of a target detection approach in hyperspectral images directly from compressive measurements without first reconstructing the full data cube that represents the real image.