Dr. Luis O. Jiménez-Rodríguez Profile

Dr. Luis O. Jiménez-Rodríguez

Assistant Professor at Univ de Puerto Rico/Mayagüez

SPIE Involvement:

Author

Publications (19)

Proceedings Article | 7 May 2007 Paper

New developments and application of the UPRM MATLAB hyperspectral image analysis toolbox

Samuel Rosario-Torres, Miguel Vélez-Reyes, Shawn Hunt, Luis Jiménez

Proceedings Volume 6565, 65650E (2007) https://doi.org/10.1117/12.721051

KEYWORDS: Algorithm development, Hyperspectral imaging, Image analysis, MATLAB, Image processing, Visualization, Remote sensing, Image visualization, Image enhancement, RGB color model

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SPIE Journal Paper | 1 April 2007

Land cover and benthic habitat classification using texture features from hyperspectral and multispectral images

Vidya Manian, Luis Jiménez-Rodríguez

JEI, Vol. 16, Issue 02, 023011, (April 2007) https://doi.org/10.1117/12.10.1117/1.2731810

KEYWORDS: Image classification, Hyperspectral imaging, Wavelets, Multispectral imaging, Earth observing sensors, High resolution satellite images, Feature selection, Feature extraction, Spatial resolution, Vegetation

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Proceedings Article | 13 October 2006 Paper

Benthic habitat mapping using hyperspectral remote sensing

Miguel Vélez-Reyes, James Goodman, Alexey Castrodad-Carrau, Luis Jiménez-Rodriguez, Shawn Hunt, Roy Armstrong

Proceedings Volume 6360, 63600C (2006) https://doi.org/10.1117/12.692996

KEYWORDS: Remote sensing, Optical properties, Reflectivity, Water, RGB color model, Hyperspectral imaging, Absorption, Animal model studies, Visualization, Nonlinear optics

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Proceedings Article | 20 October 2005 Paper

Subsurface object recognition by means of regularization techniques for mapping coastal waters floor

Luis Jiménez-Rodríguez, Alejandra Umana-Diaz, Jose Diaz-Santos, Gerardino Neira-Carolina, Javier Morales-Morales, Eladio Rodriguez

Proceedings Volume 5977, 59770H (2005) https://doi.org/10.1117/12.627011

KEYWORDS: Sensors, Remote sensing, Absorption, Algorithm development, Mathematical modeling, Water, Associative arrays, Inverse problems, Scattering, Image processing

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Proceedings Article | 19 October 2005 Paper

A comparison of statistical and multiresolution texture features for improving hyperspectral image classification

Vidya Manian, Luis Jimenez-Rodriguez, Miguel Velez-Reyes

Proceedings Volume 5982, 59820I (2005) https://doi.org/10.1117/12.627634

KEYWORDS: Hyperspectral imaging, Image classification, Wavelets, Feature selection, Image filtering, Feature extraction, Optical filters, Neural networks, Image sensors, Remote sensing

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Proceedings Article | 1 June 2005 Paper

An update on the MATLAB hyperspectral image analysis toolbox

Samuel Rosario-Torres, Emmanuel Arzuaga-Cruz, Miguel Velez-Reyes, Luis Jimenez-Rodriguez

Proceedings Volume 5806, (2005) https://doi.org/10.1117/12.605674

KEYWORDS: MATLAB, Algorithm development, Hyperspectral imaging, Image analysis, Image processing, Image enhancement, Image classification, Visualization, Multispectral imaging, Remote sensing

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Proceedings Article | 12 August 2004 Paper

Performance of eight cluster validity indices on hyperspectral data

Felix Fontan, Luis Jimenez

Proceedings Volume 5425, (2004) https://doi.org/10.1117/12.542614

KEYWORDS: Fuzzy logic, Distance measurement, Matrices, Silicon, Hyperspectral imaging, Sensors, Detection and tracking algorithms, Data centers, Missiles, Image sensors

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Proceedings Article | 7 January 2004 Paper

Clutter modeling for subsurface detection in hyperspectral imagery using Markov random fields

Yahya Masalmah, Miguel Velez-Reyes, Luis Jimenez-Rodríguez

Proceedings Volume 5159, (2004) https://doi.org/10.1117/12.507814

KEYWORDS: Data modeling, Hyperspectral imaging, Detection and tracking algorithms, Scattering, Image processing, Optical filters, Biomedical optics, Nickel, Remote sensing, Spatial resolution

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Proceedings Article | 23 September 2003 Paper

Subsurface detection of coral reefs in shallow waters using hyperspectral data

Eladio Rodriguez-Diaz, Luis Jimenez-Rodriguez, Miguel Velez-Reyes, Fernando Gilbes, Charles DiMarzio

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.486311

KEYWORDS: Sensors, Absorption, Remote sensing, Scattering, Hyperspectral imaging, Mathematical modeling, Image classification, Image processing, Algorithm development, Image resolution

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Proceedings Article | 23 September 2003 Paper

Unsupervised feature extraction and band subset selection techniques based on relative entropy criteria for hyperspectral data analysis

Emmanuel Arzuaga-Cruz, Luis Jimenez-Rodriguez, Miguel Velez-Reyes

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.485942

KEYWORDS: Principal component analysis, Feature extraction, Data analysis, Hyperspectral imaging, Remote sensing, Image processing, Data modeling, Optimization (mathematics), Image analysis, Pattern recognition

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Proceedings Article | 23 September 2003 Paper

Regularization techniques and parameter estimation for object detection in hyperspectral data

Mabel Ramirez-Velez, Luis Jimenez-Rodriguez

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.486371

KEYWORDS: Statistical analysis, Sensors, Error analysis, Visualization, Hyperspectral imaging, Image classification, Remote sensing, Nickel, Pattern recognition, Data analysis

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Proceedings Article | 23 September 2003 Paper

Hough transform for robust segmentation of underwater multispectral images

Francisco Rivera-Maldonado, Raul Torres-Muniz, Luis Jimenez-Rodriguez

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.485919

KEYWORDS: Image segmentation, Image processing algorithms and systems, Multispectral imaging, Hough transforms, Fuzzy logic, Detection and tracking algorithms, Sensors, Image filtering, Iron, Algorithm development

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Proceedings Article | 8 May 2003 Paper

Image reconstruction and subsurface detection by the application of Tikhonov regularization to inverse problems in hyperspectral images

Luis Jiminez-Rodriguez, Eladio Rodriguez-Diaz, Miguel Velez-Reyes, Charles DiMarzio

Proceedings Volume 4892, (2003) https://doi.org/10.1117/12.467313

KEYWORDS: Absorption, Scattering, Sensors, Hyperspectral imaging, Remote sensing, Mathematical modeling, Image restoration, Image classification, Inverse problems, Error analysis

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Proceedings Article | 13 March 2003 Paper

Unsupervised feature extraction techniques for hyperspectral data and its effects on unsupervised classification

Luis Jimenez-Rodriguez, Emmanuel Arzuaga-Cruz, Miguel Velez-Reyes

Proceedings Volume 4885, (2003) https://doi.org/10.1117/12.463521

KEYWORDS: Principal component analysis, Feature extraction, Landsat, Hyperspectral imaging, Remote sensing, Data analysis, Data modeling, Sensors, Data centers, Earth observing sensors

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Feature extraction, implemented as a linear projection from a higher dimensional space to a lower dimensional subspace, is a very important issue in hyperspectral data analysis. The projection must be done in a matter that minimizes the redundancy, maintaining the information content. In hyperspectral data analysis, a relevant objective of feature extraction is to reduce the dimensionality of the data maintaining the capability of discriminating object of interest from the cluttered background. This paper presents a comparative study of different unsupervised feature extraction mechanisms and shows their effects on unsupervised detection and classification. The mechanisms implemented and compared are an unsupervised SVD based band subset selection mechanism, Projection Pursuit, and Principal Component Analysis. For purposes of validating the unsupervised methods, supervised mechanisms as Discriminant Analysis and a supervised band subset selection using Bhattacharyya distance were implemented and its results were compared with the unsupervised methods. Unsupervised band subset selection based on SVD chooses automatically the most independent set of bands. Projection Pursuit based feature extraction algorithm automatically searches for projections that optimize a projection index. The projection index we optimized is one that measures the information divergence between the probability density function of the projected data and the Gaussian probability density function. This produces a projection where the probability density function of the whole data set is multi-modal, instead of a Gaussian uni-modal distribution. This augments the separability of the unknown clusters in the lower dimensional space. Finally they were compared with well-known and used Principal Component Analysis. The methods were tested using synthetic as well as remotely sensed data obtained from AVIRIS and LANDSAT. They were compared using unsupervised classification methods in a known ground truth area.

Proceedings Article | 2 August 2002 Paper

Two-stage band selection algorithm for hyperspectral imagery

Miguel Velez-Reyes, Daphnia Linares, Luis Jimenez-Rodriguez

Proceedings Volume 4725, (2002) https://doi.org/10.1117/12.478766

KEYWORDS: Hyperspectral imaging, Dimension reduction, Sensors, Data modeling, Mining, Optimization (mathematics), Chemical elements, Principal component analysis, Algorithm development, Image sensors

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Proceedings Article | 28 January 2002 Paper

Unsupervised data fusion for hyperspectral imaging

Luis Jimenez-Rodriguez, Miguel Velez-Reyes, Jorge Rivera-Medina, Hector Velasquez

Proceedings Volume 4541, (2002) https://doi.org/10.1117/12.454152

KEYWORDS: Data fusion, Hyperspectral imaging, Image fusion, Remote sensing, Sensors, Dimension reduction, Data modeling, Image processing, Image classification, Statistical analysis

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Proceedings Article | 23 August 2000 Paper

Comparison of matrix factorization algorithms for band selection in hyperspectral imagery

Miguel Velez-Reyes, Luis Jimenez-Rodriguez, Daphnia Linares, Hector Velazquez

Proceedings Volume 4049, (2000) https://doi.org/10.1117/12.410351

KEYWORDS: Hyperspectral imaging, Landsat, Sensors, Dimension reduction, Feature extraction, Image sensors, Statistical analysis, Data modeling, Image processing, Earth observing sensors

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Hyperspectral imaging sensors provide high-spectral resolution images about natural phenomena in hundreds of bands. High storage and transmission requirements, computational complexity, and statistical modeling problems motivate the idea of dimension reduction using band selection. The optimal band-selection problem can be formulated as a combinatorial optimization problem where p-bands from a set of n-bands are selected such that some measure of information content is maximized. Potential applications for automated band selection include classifier feature extraction, and band location in sensor design and in programming of reconfigurable sensors. The computational requirements for standard search algorithms to solve the optimal band selection problem are prohibitive. In this paper, we present the use of singular value and rank revealing QR matrix factorizations for band selection. These matrix factorizations can be use to determine the most independent columns of a matrix. The selected columns represent the most independent bands that contain most of the spatial information. It can be shown that under certain circumstances, the bands selected using these matrix factorizations are good approximations to the principal components explaining most of the image spatial variability. The advantage of matrix factorizations over the combinatorial optimization approach is that they take polynomial time and robust and proven numerical routines for their computation and are readily available from many sources. In the paper, we will present results comparing the performance of the algorithms using AVIRIS and LANDSAT imagery. Algorithms are compared in their computational requirements, their capacity to approximate the principal components, and their performance as an automated feature extraction processor in a classification algorithm. Preliminary results show that under certain circumstances selected bands can have over 90% correlation with principal components and classifiers using these algorithms in feature extraction can outperform spectral angle classifiers.

Proceedings Article | 14 December 1999 Paper

Integration of spatial and spectral information in unsupervised classification for multispectral and hyperspectral data

Luis Jimenez-Rodriguez, Jorge Rivera-Medina

Proceedings Volume 3871, (1999) https://doi.org/10.1117/12.373270

KEYWORDS: Information fusion, Remote sensing, Statistical analysis, Image classification, Detection and tracking algorithms, Distance measurement, Optical filters, Image processing, Classification systems, Pattern recognition

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Proceedings Article | 16 July 1999 Paper

Partially supervised detection using band subset selection in hyperspectral data

Luis Jimenez-Rodriguez, Miguel Velez-Reyes, Yamil Chaar, Felix Fontan, Cesar Santiago, Roberto Hernandez

Proceedings Volume 3717, (1999) https://doi.org/10.1117/12.353032

KEYWORDS: Sensors, Fuzzy logic, Pattern recognition, Dimension reduction, Remote sensing, Spectral resolution, Hyperspectral imaging, Detection and tracking algorithms, Data modeling, Optical filters

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Showing 5 of 19 publications

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