Thesis (Ph.D.). / Written for the Dept. of Chemistry. Title from title page of PDF (viewed 2009/06/08). Includes bibliographical references.

Spatial variations in soil properties can directly affect Nitrogen status of corn (Zea mays L.) and decrease efficiency of uniform fertilizer N applications. The objective of this study was to assess the spatial variations of soil properties and measure the effect on corn Nitrogen status through canopy reflectance. Field trials were conducted in 2010 and 2011 on a producer’s field west of Yazoo City, MS that contained high in field variability. Soil physical and chemical properties all exhibited moderate to high spatial dependency during both years of this study. Vegetative indices were derived from canopy reflectance values and indices utilizing the red-edge were the strongest and most consistent descriptors of tissue N percent and whole plant N uptake. The Canopy Chlorophyll Content Index (I) shows the greatest potential of assessing variations of corn Nitrogen status among the indices tested.

Multispectral imaging systems provide much of the basic data used by the land and ocean civilian remote sensing community. There are numerous multispectral imaging systems which have been and are being developed. A common way to compare the radiometric performance of these sensors is to examine their noise equivalent change in reflectance, NEDeltarho. The NEDeltarho of a sensor is the reflectance difference that is equal to the noise in the recorded signal. In order to directly compare the sensors, calculations of the parameter being compared need to have a common basis. This thesis compares the noise equivalent change in reflectance of seven different multispectral imaging systems (AVHRR, AVIRIS, ETM, HIRIS, MODIS-N, SPOT-1/HRV, and TM) for a set of three atmospheric conditions (continental aerosol with 23 km visibility, continental aerosol with 5 km visibility, and a Rayleigh atmosphere), five values of ground reflectance (0.01, 0.10, 0.25, 0.50, and 1.00), a nadir viewing angle, and a solar zenith angle of forty-five degrees.

Many materials such as drugs and explosives have characteristic spectral signatures in the terahertz (THz) band. These unique signatures imply great promise for spectral detection and classification using THz radiation. While such spectral features are most easily observed in transmission, real-life imaging systems will need to identify materials of interest from reflection measurements, often in non-ideal geometries. One important, yet commonly overlooked source of signal corruption is the etalon effect - interference phenomena caused by multiple reflections from dielectric layers of packaging and clothing likely to be concealing materials of interest in real-life scenarios. This thesis focuses on the development and implementation of a model-based material parameter estimation technique, primarily for use in reflection spectroscopy, that takes the influence of the etalon effect into account. The technique is adapted from techniques developed for transmission spectroscopy of thin samples and is demonstrated using measured data taken at the Northwest Electromagnetic Research Laboratory (NEAR-Lab) at Portland State University. Further tests are conducted, demonstrating the technique's robustness against measurement noise and common sources of error.

We present an algorithm that uses multiple cues to recover shading and reflectance intrinsic images from a single image. Using both color information and a classifier trained to recognize gray-scale patterns, each image derivative is classified as being caused by shading or a change in the surface's reflectance. Generalized Belief Propagation is then used to propagate information from areas where the correct classification is clear to areas where it is ambiguous. We also show results on real images.

Skin cancer is the most common form of cancer and it is on the rise. If skin cancer is diagnosed early enough, the survival rate is close to 90%. Oblique-incidence diffuse reflectance (OIR) spectroscopy offers a technology that may be used in the clinic to aid physicians in diagnosing both melanoma and non-melanoma skin cancers. The system includes a halogen light source, a fiber optic probe, an imaging spectrograph, a charge coupled device (CCD) camera, and a computer. Light is delivered to the skin surface via optical fibers in the probe. After interacting with the skin, the light is collected and sent to the spectrograph that generates optical spectra. Images and histopathological diagnoses were obtained from 250 lesions at the University of Texas M.D. Anderson Cancer Center (Melanoma and Skin Center). To classify OIR data, an image processing algorithm was developed and evaluated for both pigmented and non-pigmented lesions. The continuous wavelet transform and the genetic algorithm were employed to extract optimal classification features. Bayes decision rule was used to categorize spatiospectral images based on the selected classification features. The overall classification accuracy for pigmented melanomas and severely dysplastic nevi is 100%. The overall classification accuracy for non-pigmented skin cancers and severely dysplastic nevi is 93.33%. Oblique-incidence diffuse reflectance spectroscopy and the developed algorithms have high classification rates and may prove useful in the clinic as the process is fast, noninvasive and accurate.

Includes bibliographical references (p. 124-135).

Thesis (Ph. D.)--Drexel University, 2003. / Includes abstract and vita. Includes bibliographical references (leaves 125-135).

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