Dr. Harry N. Gross Profile

Dr. Harry N. Gross

USAF Major at Integrity Applications Inc

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Publications (6)

Proceedings Article | 20 May 2011 Paper

A scalable hierarchical approach for leveraging low resolution imagery for image classification

Harry Gross, Curt Munechika, Francis Padula, David Pogorzala

Proceedings Volume 8048, 804810 (2011) https://doi.org/10.1117/12.883466

KEYWORDS: Image resolution, MODIS, Image classification, Landsat, Image processing, Earth observing sensors, Satellite imaging, Satellites, Detection and tracking algorithms, Data archive systems

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Proceedings Article | 27 April 2009 Paper

Experimental determination of bidirectional reflectance distribution at the LSpec vicarious calibration site

Harry Gross, Stanley Green

Proceedings Volume 7334, 733412 (2009) https://doi.org/10.1117/12.818488

KEYWORDS: Reflectivity, Sensors, Calibration, Sun, Bidirectional reflectance transmission function, Spectrometers, Sensor calibration, Head, Remote sensing, Data acquisition

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Proceedings Article | 27 September 2007 Paper

LED Spectrometer (LSpec) autonomous vicarious calibration facility

Harry Gross, Carol Bruegge, Ethan Lubka, Mark Helmlinger

Proceedings Volume 6677, 66770V (2007) https://doi.org/10.1117/12.730955

KEYWORDS: Reflectivity, Light emitting diodes, Sensors, Calibration, Data acquisition, Spectroscopy, Aerosols, Atmospheric particles, Radiative transfer, Infrared sensors

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Proceedings Article | 29 January 1999 Paper

Advanced synthetic image generation models and their application to multi/hyperspectral algorithm development

Scott Brown, Gary Robinson, Harry Gross, John Schott, Rolando Raqueno

Proceedings Volume 3584, (1999) https://doi.org/10.1117/12.339823

KEYWORDS: Image fusion, Algorithm development, Sensors, Image processing, Reflectivity, Thermal modeling, Digital imaging, Atmospheric modeling, Spectral resolution, Data modeling

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Proceedings Article | 6 November 1996 Paper

Application of spatial resolution enhancement and spectral mixture analysis to hyperspectral images

John Schott, Harry Gross

Proceedings Volume 2821, (1996) https://doi.org/10.1117/12.257182

KEYWORDS: Image fusion, Spatial resolution, Image resolution, Spectral resolution, Reflectivity, Hyperspectral imaging, Resolution enhancement technologies, Image analysis, Analytical research, Image processing

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Spatial resolution enhancement and spectral mixture analysis are two of the most extensively used image analysis algorithms. This paper presents an algorithm that merges the best aspects of these two techniques while trying to preserve the spectral and spatial radiometric integrity of the data. With spectral mixture analysis, the fraction of each material (endmember) in every pixel is determined from hyperspectral data. This paper describes an improved unmixing algorithm based upon stepwise regression. The result is a set of material maps where the intensity corresponds to the percentage of a particular endmember within the pixel. The maps are constructed at the spatial resolution of the hyperspectral sensor. The spatial resolution of the material maps is then enhanced using one or more higher spatial resolution images. Similar to the unmixing approach, different endmember contributions to the pixel digital counts are distinguished by the endmember reflectances in the sharpening band(s). After unmixing, the fraction maps are sharpened with a constrained optimization algorithm. This paper presents the results of an image fusion algorithm that combines spectral unmixing and spatial sharpening. Quantifiable results are obtained through the use of synthetically generated imagery. Without synthetic images, a large amount of ground truth would be required in order to measure the accuracy of the material maps. Multiple band sharpening is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. The analysis includes an examination of the effects of constraints and texture variation on the material maps. The results show stepwise unmixing is an improvement over traditional unmixing algorithms. The results also indicate sharpening improves the material maps. The motivation for this research is to take advantage of the next generation of multi/hyperspectral sensors. Although the hyperspectral images will be of modest to low resolution, fusing them with high resolution sharpening images will produce a higher spatial resolution land cover or material map.

Proceedings Article | 17 June 1996 Paper

Evaluating an image-fusion algorithm with synthetic image generation tools

Harry Gross, John Schott

Proceedings Volume 2758, (1996) https://doi.org/10.1117/12.243209

KEYWORDS: Image fusion, Spatial resolution, Image resolution, Algorithm development, Reflectivity, Spectral resolution, Optimization (mathematics), Sensors, Hyperspectral imaging, Image processing

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An algorithm that combines spectral mixing and nonlinear optimization is used to fuse multiresolution images. Image fusion merges images of different spatial and spectral resolutions to create a high spatial resolution multispectral combination. High spectral resolution allows identification of materials in the scene, while high spatial resolution locates those materials. In this algorithm, conventional spectral mixing estimates the percentage of each material (called endmembers) within each low resolution pixel. Three spectral mixing models are compared; unconstrained, partially constrained, and fully constrained. In the partially constrained application, the endmember fractions are required to sum to one. In the fully constrained application, all fractions are additionally required to lie between zero and one. While negative fractions seem inappropriate, they can arise from random spectral realizations of the materials. In the second part of the algorithm, the low resolution fractions are used as inputs to a constrained nonlinear optimization that calculates the endmember fractions for the high resolution pixels. The constraints mirror the low resolution constraints and maintain consistency with the low resolution fraction results. The algorithm can use one or more higher resolution sharpening images to locate the endmembers to high spatial accuracy. The algorithm was evaluated with synthetic image generation (SIG) tools. A SIG developed image can be used to control the various error sources that are likely to impair the algorithm performance. These error sources include atmospheric effects, mismodeled spectral endmembers, and variability in topography and illumination. By controlling the introduction of these errors, the robustness of the algorithm can be studied and improved upon. The motivation for this research is to take advantage of the next generation of multi/hyperspectral sensors. Although the hyperspectral images will be of modest to low resolution, fusing them with high resolution sharpening images will produce a higher spatial resolution land cover or material map.

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