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Open AccessArticle

Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data

1
Applied Remote Sensing Laboratory, Department of Geography, McGill University, Montréal, QC H3A 0B9, Canada
2
Flight Research Laboratory, National Research Council of Canada, Ottawa, ON K1A 0R6, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 641; https://doi.org/10.3390/rs12040641 (registering DOI)
Received: 12 January 2020 / Revised: 7 February 2020 / Accepted: 11 February 2020 / Published: 14 February 2020
In hyperspectral imaging (HSI), the spatial contribution to each pixel is non-uniform and extends past the traditionally square spatial boundaries designated by the pixel resolution, resulting in sensor-generated blurring effects. The spatial contribution to each pixel can be characterized by the net point spread function, which is overlooked in many airborne HSI applications. The objective of this study was to characterize and mitigate sensor blurring effects in airborne HSI data with simple tools, emphasizing the importance of point spread functions. Two algorithms were developed to (1) quantify spatial correlations and (2) use a theoretically derived point spread function to perform deconvolution. Both algorithms were used to characterize and mitigate sensor blurring effects on a simulated scene with known spectral and spatial variability. The first algorithm showed that sensor blurring modified the spatial correlation structure in the simulated scene, removing 54.0%–75.4% of the known spatial variability. Sensor blurring effects were also shown to remove 31.1%–38.9% of the known spectral variability. The second algorithm mitigated sensor-generated spatial correlations. After deconvolution, the spatial variability of the image was within 23.3% of the known value. Similarly, the deconvolved image was within 6.8% of the known spectral variability. When tested on real-world HSI data, the algorithms sharpened the imagery while characterizing the spatial correlation structure of the dataset, showing the implications of sensor blurring. This study substantiates the importance of point spread functions in the assessment and application of airborne HSI data, providing simple tools that are approachable for all end-users. View Full-Text
Keywords: hyperspectral imaging; point spread function; spatial correlations; image deconvolution hyperspectral imaging; point spread function; spatial correlations; image deconvolution
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MDPI and ACS Style

Inamdar, D.; Kalacska, M.; Leblanc, G.; Arroyo-Mora, J.P. Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data. Remote Sens. 2020, 12, 641.

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