Dr. Hamed Parsiani Profile

Dr. Hamed Parsiani

Professor at Univ de Puerto Rico/Mayaguez

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Publications (5)

Proceedings Article | 10 November 2004 Paper

Subsurface material type determination from ground-penetrating radar signatures

Hamed Parsiani, Pedro Rodriguez

Proceedings Volume 5573, (2004) https://doi.org/10.1117/12.566481

KEYWORDS: General packet radio service, Wavelets, Reflection, Ground penetrating radar, Neural networks, Algorithm development, Antennas, Dielectrics, Electromagnetism, Electromagnetic radiation

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Proceedings Article | 10 November 2004 Paper

High-resolution vegetation index as measured by radar and its validation with spectrometer

Hamed Parsiani, Maritza Torres, Pedro Rodriguez

Proceedings Volume 5573, (2004) https://doi.org/10.1117/12.566436

KEYWORDS: Vegetation, Spectroscopy, General packet radio service, Satellites, Sensors, Near infrared, Backscatter, Radar, Synthetic aperture radar, Active sensors

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Changes in vegetation can affect our health, the environment and the economy. Understanding this, twenty years ago scientists began to use satellite remote sensors to monitor major fluctuations in vegetation and understand how it affects the environment. The pixel accuracy of some Synthetic Aperture Radar (SAR) satellites is now at near one meter resolution. A new formulation of vegetation index using such active sensors will greatly improve the Vegetation health accuracy. Attempt has been made by M. Tokunaga to relate ERS-SAR satellite sensor data of vegetation canopies to the LANDSAT TM satellite sensor measurements, both at 30 meter resolution. A correlation was observed above Normalized Difference Vegetation Index (NDVI) of 0.4, but their experiment was not based on the data taken by the two satellite sensors at the same time period. In this research, a correlation is determined between the active and passive measurements of the vegetation index, at very high resolution. The measurements take place at the near ground level over varied vegetation health, using a Ground Penetrating Radar (GPR), and a handheld Spectrometer. The GPR and the handheld Spectrometer have the same field of view, so it is possible to compare data for the whole range of NDVI. Both measurements take place one right after the other, to allow an accurate comparison. The goal of this research is to define a new vegetation index, using active sensors. The GPR operating at 1.5 GHz produces images that contain backscatter signals obtained from vegetation. These images are processed by a filter to eliminate clutter and noise. The Fourier amplitude and phase characteristics of the vegetation health are extracted from the backscatter signal. The same vegetation is subjected to the spectrometer measurements. Our results show a linear correlation between power of GPR backscatter signal and the NDVI as calculated by the spectrometer data. As a continuity of this work, the ground validation will be compared to the active/passive satellite sensors for the measurement of vegetation health.

Proceedings Article | 5 February 2004 Paper

Application of morphological associative memories and Fourier descriptors for classification of noisy subsurface signatures

Jorge Ortiz, Hamed Parsiani, Leonid Tolstoy

Proceedings Volume 5238, (2004) https://doi.org/10.1117/12.511074

KEYWORDS: Content addressable memory, Image processing, Matrices, General packet radio service, Neural networks, Prototyping, Binary data, Ground penetrating radar, Complex systems, Radar

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Proceedings Article | 5 February 2004 Paper

Application of Fourier descriptors and fuzzy logic to classification of radar subsurface images

Hamed Parsiani, Leonid Tolstoy

Proceedings Volume 5238, (2004) https://doi.org/10.1117/12.510929

KEYWORDS: Fuzzy logic, Image classification, General packet radio service, Radar, Detection and tracking algorithms, Image filtering, Filtering (signal processing), Ground penetrating radar, Backscatter, Antennas

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Proceedings Article | 13 March 2003 Paper

Application of Fourier descriptors and neural network to classification underground images

Leonid Tolstoy, Hamed Parsiani, Jorge Ortiz

Proceedings Volume 4885, (2003) https://doi.org/10.1117/12.463192

KEYWORDS: Neural networks, General packet radio service, Image classification, Detection and tracking algorithms, Evolutionary algorithms, 3D image processing, Image filtering, Visualization, Filtering (signal processing), Radar

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