Dr. Pamela Blake Profile

Dr. Pamela Blake

Retired at Self

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Author | Science Fair Judge

Publications (2)

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Proceedings Article | 20 August 2001 Paper

Hyperspectral materials detection/identification/quantification using the residual correlation method

Lonnie Hudgins, Joan Hayashi, Pamela Blake, Luong Tran, Elizabeth Kilday

Proceedings Volume 4381, (2001) https://doi.org/10.1117/12.437029

KEYWORDS: Chemical elements, Signal to noise ratio, Binary data, Reflectivity, Image processing, Detection and tracking algorithms, Sensors, Error analysis, Hyperspectral imaging, Algorithm development

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Remote detection, identification, and quantification of materials is an important problem in earth resource assessment. Satellite-based hyperspectral imaging sensors currently being developed by government and industry partnerships (e.g. the Coastal Ocean Imaging Spectrometer aboard the Naval EarthMap Observer) appear to be uniquely qualified for this purpose. Obtaining accurate estimates of material abundance on a pixel-by-pixel basis poses many challenging algorithmic and computational difficulties. A significant issue that must be addressed is how to efficiently select endmembers from a library when that library is spectrally redundant. In this paper, we demonstrate how an improved version of the Residual Correlation Method (RCM+) can provide a flexible solution to this problem. The RCM+ offers a robust treatment for selecting endmembers from spectrally redundant libraries in a one-at-a-time fashion. We discuss alternative methods such as two-at-a-time, or more generally, N-at-a-time methods within a unified mathematical framework for analysis. Certain theorems apply to all such methods, and help to define a trade space for endmember selection methods in general. We demonstrate our results using synthetic test cases, and discuss how all endmember selection methods may be affected by redundancy within the library as well as specific properties of the data.

Proceedings Article | 17 June 1996 Paper

Subpixel material identification by residual correlation

Pamela Blake, Gerald Pellegrini, Mark Vriesenga

Proceedings Volume 2758, (1996) https://doi.org/10.1117/12.243241

KEYWORDS: Minerals, Databases, Calibration, Data modeling, Chemical elements, Reflectivity, Sensors, Absorption, Mathematical modeling, Analytical research

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The recognition of subpixel signatures is critical to realizing the full detection potential of multispectral and hyperspectral sensors. No approach has been developed that optimizes and fully characterizes the subpixel spectral components independently for every pixel in a data set. Such a full characterization is important because a target or material of interest may appear against a variety of background types in the same scene, and will undoubtedly be more distinguishable against some background types than others. Further, characterization of ground reflectance on a pixel-by-pixel basis is important for validating the quality of the atmospheric calibration results. We have developed an approach called the residual correlation method (RCM) for performing a full decomposition of each pixel into its component spectral elements. In this paper we describe preliminary results for the application of the RCM to hyperspectral pixel data. The work reported in this paper is from the first phase of a three phase research project. In this phase we develop the basic methodology for subpixel material identification and test it against hyperspectral data for a well-known area. The RCM determines the presence of minerals and gives a linear approximation of the abundances of the minerals in each pixel. PHase one performs a nominal atmospheric calibration using a simple normalization technique. The second phase will be to determine more precise mineral abundances using a nonlinear demixing approach based on the band shape of relevant absorption features. Phase two will also explore various methods of presenting the results of a full demixing for each pixel. Phase three of this research will be to perform a more rigorous atmospheric calibration and to include that approach as an intrinsic part of the RCM.

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