Dr. Ryan A. Mercovich Profile

Dr. Ryan A. Mercovich

at URSA

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Publications (3)

Proceedings Article | 21 May 2015 Paper

Evaluation techniques and metrics for assessment of pan+MSI fusion (pansharpening)

Ryan Mercovich

Proceedings Volume 9472, 94721I (2015) https://doi.org/10.1117/12.2177093

KEYWORDS: Multispectral imaging, Image fusion, Image quality, Visualization, Image analysis, Data fusion, Principal component analysis, Composites, Image resolution, Spatial frequencies

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Fusion of broadband panchromatic data with narrow band multispectral data – pansharpening – is a common and often studied problem in remote sensing. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. This study examines the output products of 4 commercial implementations with regard to their relative strengths and weaknesses for a set of defined image characteristics and analyst use-cases. Image characteristics used are spatial detail, spatial quality, spectral integrity, and composite color quality (hue and saturation), and analyst use-cases included a variety of object detection and identification tasks. The imagery comes courtesy of the RIT SHARE 2012 collect. Two approaches are used to evaluate the pansharpening methods, analyst evaluation or qualitative measure and image quality metrics or quantitative measures. Visual analyst evaluation results are compared with metric results to determine which metrics best measure the defined image characteristics and product use-cases and to support future rigorous characterization the metrics’ correlation with the analyst results. Because pansharpening represents a trade between adding spatial information from the panchromatic image, and retaining spectral information from the MSI channels, the metrics examined are grouped into spatial improvement metrics and spectral preservation metrics. A single metric to quantify the quality of a pansharpening method would necessarily be a combination of weighted spatial and spectral metrics based on the importance of various spatial and spectral characteristics for the primary task of interest. Appropriate metrics and weights for such a combined metric are proposed here, based on the conducted analyst evaluation. Additionally, during this work, a metric was developed specifically focused on assessment of spatial structure improvement relative to a reference image and independent of scene content. Using analysis of Fourier transform images, a measure of high-frequency content is computed in small sub-segments of the image. The average increase in high-frequency content across the image is used as the metric, where averaging across sub-segments combats the scene dependent nature of typical image sharpness techniques. This metric had an improved range of scores, better representing difference in the test set than other common spatial structure metrics.

Proceedings Article | 24 May 2012 Paper

Automatically enumerating image data clusters using pixel co-density

Ryan Mercovich

Proceedings Volume 8390, 83901S (2012) https://doi.org/10.1117/12.919039

KEYWORDS: Clouds, Buildings, Digital imaging, Image processing, Distance measurement, Data analysis, Data processing, Convolution, Smoothing, MODIS

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Proceedings Article | 20 May 2011 Paper

Automatic clustering of multispectral imagery by maximization of the graph modularity

Ryan Mercovich, Anthony Harkin, David Messinger

Proceedings Volume 8048, 80480Z (2011) https://doi.org/10.1117/12.884146

KEYWORDS: Image processing, Data modeling, Composites, Visualization, Multispectral imaging, Image visualization, Image analysis, Image classification, Distance measurement, Clouds

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