Characterization and modelling of the spatially- and spectrally-varying point-spread function in hyperspectral imaging systems for computational correction of axial optical aberrations

The Point Spread Function (PSF) of the astronomical imaging system is usually approximated by a Gaussian or Moffat function. For simplification, the astronomical imaging system is considered to be time and space invariant. This means that invariable PSF within an exposed image is assumed. If real wide-field imaging systems are considered, this presumption is not fulfilled. In real systems, stronger optical aberrations are expected (especially coma) at greater distances from the center of the captured image. This impacts the efficiency of stellar astrometry and photometry algorithms, so it is necessary to know the PSF variation. In this paper, we perform the first step toward assigning PSF changes: we study the dependence of the Moffat function fitting parameters (FWHM and the atmospheric scattering coefficient ) on the position of a stellar object.

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

Remote Sensing ◽

10.3390/rs12040641 ◽

2020 ◽

Vol 12 (4) ◽

pp. 641 ◽

Cited By ~ 3

Author(s):

Deep Inamdar ◽

Margaret Kalacska ◽

George Leblanc ◽

J. Pablo Arroyo-Mora

Keyword(s):

Spatial Variability ◽

Spatial Correlation ◽

Hyperspectral Imaging ◽

Point Spread Function ◽

Spatial Correlations ◽

Spectral Variability ◽

Point Spread Functions ◽

Point Spread ◽

Spatial Correlation Structure ◽

Spread Function

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.

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