Mr. Hector M. Vargas Garcia Profile

Hector M. Vargas Garcia

at Univ Industrial de Santander

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Proceedings Article | 10 October 2018 Presentation + Paper

Unmixing-based approach as a tool for classification of oil palm diseases using hyperspectral remote sensing in Colombia

Ariolfo Camacho, Hector Vargas, Henry Arguello

Proceedings Volume 10783, 1078312 (2018) https://doi.org/10.1117/12.2501805

KEYWORDS: Hyperspectral imaging, Remote sensing, Image processing, Agriculture, Image classification

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Hyperspectral remote sensing has the potential to provide quantitative information on the spatial cover, acquiring relevance for the agronomic management. Traditionally, the diagnosis, management, and control of diseases in oil palm crops is a time-consuming and difficult task given that it needs a visual symptom observation. Currently, oil palm crops deal with diseases and infections. The bud rot disease (PC in Spanish) of the oil palm is one of the most common diseases in Central and South American countries, especially in Colombia. A viable alternative for the identification of diseased palms is the use of hyperspectral images and classification algorithms. Nevertheless, the usual assumption that every pixel of the hyperspectral image can be associated with a unique class label is no longer verified, and mixed pixels cannot be correctly addressed by traditional classifiers. This paper presents an unmixing-based approach as a tool for classification of stress oil palms caused by the bud rot disease, conducted on hyperspectral datasets of oil palm crops from Colombia, through the estimation of abundance maps with three labels: diseased oil palm, healthy oil palm and background (grass-shadow).

Proceedings Article | 15 October 2015 Paper

Colored coded-apertures for spectral image unmixing

Hector Vargas, Henry Arguello Fuentes

Proceedings Volume 9643, 964320 (2015) https://doi.org/10.1117/12.2194611

KEYWORDS: Coded apertures, Staring arrays, 3D metrology, Imaging systems, Associative arrays, Data modeling, Image compression, Imaging spectroscopy, Signal to noise ratio, Spatial resolution

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Hyperspectral remote sensing technology provides detailed spectral information from every pixel in an image. Due to the low spatial resolution of hyperspectral image sensors, and the presence of multiple materials in a scene, each pixel can contain more than one spectral signature. Therefore, endmember extraction is used to determine the pure spectral signature of the mixed materials and its corresponding abundance map in a remotely sensed hyperspectral scene. Advanced endmember extraction algorithms have been proposed to solve this linear problem called spectral unmixing. However, such techniques require the acquisition of the complete hyperspectral data cube to perform the unmixing procedure. Researchers show that using colored coded-apertures improve the quality of reconstruction in compressive spectral imaging (CSI) systems under compressive sensing theory (CS). This work aims at developing a compressive supervised spectral unmixing scheme to estimate the endmembers and the abundance map from compressive measurements. The compressive measurements are acquired by using colored coded-apertures in a compressive spectral imaging system. Then a numerical procedure estimates the sparse vector representation in a 3D dictionary by solving a constrained sparse optimization problem. The 3D dictionary is formed by a 2-D wavelet basis and a known endmembers spectral library, where the Wavelet basis is used to exploit the spatial information. The colored coded-apertures are designed such that the sensing matrix satisfies the restricted isometry property with high probability. Simulations show that the proposed scheme attains comparable results to the full data cube unmixing technique, but using fewer measurements.

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