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12 April 2021 Subpixel target implantation to assess pansharpening performance on hyperspectral datasets
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Target detection is one of the most important applications utilizing the rich spectral information from hyperspectral imaging systems. Data fusion algorithms applied on hyperspectral datasets address the inherent spatial-spectral resolution tradeoff in these imaging systems by combining spectral information from hyperspectral data with spatial information from hi-res panchromatic or multispectral images (e.g., hi-res RGB). This paper presents the first attempt at using an iterative target implantation technique as a modification to Wald's protocol to assess the performance of data fusion algorithms in target detection tasks. More specifically, this paper looks at how the sharpening process localizes and discriminates the subpixel target from its background, and characterizes an image-wide detectability of any single subpixel target independent of location in the image. We used NNDi use as our pansharpener to perform HRPAN+LRHSI data fusion and the adaptive coherence estimator (ACE) as our target detector. Results show that our methodology is effective at assessing (1) how the sharpening process enhances target-background separability within any 5x5 window anywhere on the image and (2) how the sharpening process enhances the detectability of a single subpixel target over the entire hyperspectral image.
Conference Presentation
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Rey Ducay and David W. Messinger "Subpixel target implantation to assess pansharpening performance on hyperspectral datasets", Proc. SPIE 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, 117270Z (12 April 2021);

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