Dr. Gabriel Martín Profile

Dr. Gabriel Martín

at Instituto de Telecomunicaçoes

SPIE Involvement:

Author

Publications (11)

Proceedings Article | 5 October 2017 Presentation + Paper

On the use of Jetson TX1 board for parallel hyperspectral compressive sensing

José M. Nascimento, Gabriel Martin

Proceedings Volume 10430, 1043002 (2017) https://doi.org/10.1117/12.2278050

KEYWORDS: Hyperspectral imaging, Matrices, Compressed sensing, Signal processing, Image processing, Sensors, Image compression, Reconstruction algorithms, Image sensors, Graphics processing units

Read Abstract +

Proceedings Article | 24 October 2016 Presentation + Paper

Parallel hyperspectral image reconstruction using random projections

Jorge Sevilla, Gabriel Martín, José M. Nascimento

Proceedings Volume 10007, 1000707 (2016) https://doi.org/10.1117/12.2241252

KEYWORDS: Hyperspectral imaging, Compressed sensing, Image restoration, Graphics processing units, Matrices, Computing systems, Reconstruction algorithms, Computer architecture, Telecommunications, Chemical elements

Read Abstract +

Proceedings Article | 29 April 2016 Paper

Parallel implementation of a hyperspectral data geometry-based estimation of number of endmembers algorithm

Sergio Bernabé , Gabriel Martin, Guillermo Botella, Manuel Prieto-Matias, Antonio Plaza

Proceedings Volume 9897, 989708 (2016) https://doi.org/10.1117/12.2227910

KEYWORDS: Graphics processing units, Data analysis, Hyperspectral imaging, Computer graphics, Remote sensing, Monte Carlo methods, Hyperspectral systems, Image processing, Video processing, Image compression, Matrix multiplication, Minerals

Read Abstract +

Proceedings Article | 20 October 2015 Paper

Parallel hyperspectral compressive sensing method on GPU

Sergio Bernabé, Gabriel Martín, José Nascimento

Proceedings Volume 9646, 96460P (2015) https://doi.org/10.1117/12.2194520

KEYWORDS: Compressed sensing, Hyperspectral imaging, Matrices, Image compression, Signal processing, Reconstruction algorithms, Graphics processing units, Computer architecture, Remote sensing, Coded apertures

Read Abstract +

Proceedings Article | 19 October 2012 Paper

A new technique for hyperspectral compressive sensing using spectral unmixing

Gabriel Martin, José Bioucas Dias, Antonio Plaza

Proceedings Volume 8514, 85140N (2012) https://doi.org/10.1117/12.964374

KEYWORDS: Hyperspectral imaging, Reconstruction algorithms, Compressed sensing, Image compression, Image restoration, Signal to noise ratio, Sensors, Monte Carlo methods, Coded apertures, Algorithm development

Read Abstract +

Proceedings Article | 17 September 2011 Paper

Joint spectral and spatial preprocessing prior to endmember extraction from hyperspectral images

Gabriel Martín, Antonio Plaza

Proceedings Volume 8157, 81570U (2011) https://doi.org/10.1117/12.892286

KEYWORDS: Hyperspectral imaging, Fractal analysis, Algorithm development, Signal to noise ratio, Gaussian filters, Image filtering, Spatial resolution, Clouds, Imaging systems, Error analysis

Read Abstract +

Proceedings Article | 24 August 2010 Paper

GPU implementation of fully constrained linear spectral unmixing for remotely sensed hyperspectral data exploitation

Sergio Sánchez, Gabriel Martín, Antonio Plaza, Chein-I Chang

Proceedings Volume 7810, 78100G (2010) https://doi.org/10.1117/12.860775

KEYWORDS: Hyperspectral imaging, Vegetation, Algorithm development, Data modeling, Reconstruction algorithms, Image processing, Computer architecture, Data communications, Computing systems, Spectroscopy

Read Abstract +

SPIE Journal Paper | 1 July 2010

Impact of JPEG2000 compression on endmember extraction and unmixing of remotely sensed hyperspectral data

Gabriel Martin, Vicente González Ruiz, Antonio Plaza, Juan Ortiz, Inmaculada García Fernández

JARS, Vol. 4, Issue 01, 041796, (July 2010) https://doi.org/10.1117/12.10.1117/1.3474975

KEYWORDS: Image compression, JPEG2000, Hyperspectral imaging, Image quality, Algorithms, Wavelets, Computed tomography, Reconstruction algorithms, Algorithm development, Discrete wavelet transforms

Read Abstract +

Proceedings Article | 5 May 2010 Paper

Near real-time endmember extraction from remotely sensed hyperspectral data using NVidia GPUs

Sergio Sánchez, Gabriel Martín, Abel Paz, Antonio Plaza, Javier Plaza

Proceedings Volume 7724, 772409 (2010) https://doi.org/10.1117/12.854365

KEYWORDS: Hyperspectral imaging, Image processing, Algorithm development, Sensors, Minerals, Computing systems, Spectroscopy, Spatial resolution, Statistical analysis, Spectral resolution

Read Abstract +

One of the most important techniques for hyperspectral data exploitation is spectral unmixing, which aims at characterizing mixed pixels. When the spatial resolution of the sensor is not fine enough to separate different spectral constituents, these can jointly occupy a single pixel and the resulting spectral measurement will be a composite of the individual pure spectra. The N-FINDR algorithm is one of the most widely used and successfully applied methods for automatically determining endmembers (pure spectral signatures) in hyperspectral image data without using a priori information. The identification of such pure signatures is highly beneficial in order to 'unmix' the hyperspectral scene, i.e. to perform sub-pixel analysis by estimating the fractional abundance of endmembers in mixed pixels collected by a hyperspectral imaging spectrometer. The N-FINDR algorithm attempts to automatically find the simplex of maximum volume that can be inscribed within the hyperspectral data set. Due to the intrinsic complexity of remotely sensed scenes and their ever-increasing spatial and spectral resolution, the efficiency of the endmember searching process conducted by N-FINDR depends not only on the size and dimensionality of the scene, but also on its complexity (directly related with the number of endmembers). In this paper, we develop a new parallel version of N-FINDR which is shown to scale better as the dimensionality and complexity of the hyperspectral scene to be processed increases. The parallel algorithm has been implemented on two different parallel systems, in which two different types of commodity graphics processing units (GPUs) from NVidia™ are used to assist the CPU as co-processors. Commodity computing in GPUs is an exciting new development in remote sensing applications since these systems offer the possibility of (onboard) high performance computing at very low cost. Our experimental results, obtained in the framework of a mineral mapping application using hyperspectral data collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS), reveal that the proposed parallel implementation compares favorably with the original version of N-FINDR not only in terms of computation time, but also in terms of the the accuracy of the solutions that it provides. The real-time processing capabilities of our GPU-based N-FINDR algorithms and other GPU algorithms for endmember extraction are also discussed.

Proceedings Article | 28 September 2009 Paper

A fast sequential endmember extraction algorithm based on unconstrained linear spectral unmixing

Javier Plaza, Antonio Plaza, Gabriel Martín

Proceedings Volume 7477, 74770L (2009) https://doi.org/10.1117/12.830734

KEYWORDS: Algorithm development, Signal to noise ratio, Data modeling, Minerals, Hyperspectral imaging, Visible radiation, Spectroscopy, Image processing, Feature extraction, Vegetation

Read Abstract +

Proceedings Article | 31 August 2009 Paper

Impact of JPEG2000 compression on spatial-spectral endmember extraction from hyperspectral data

Gabriel Martín, V. Ruiz, Antonio Plaza, Juan Ortiz, Inmaculada García

Proceedings Volume 7455, 74550A (2009) https://doi.org/10.1117/12.826318

KEYWORDS: Discrete wavelet transforms, Image compression, JPEG2000, Image quality, Hyperspectral imaging, Computed tomography, Wavelets, Image processing, Computer programming, Data compression

Read Abstract +

Showing 5 of 11 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years