Abstract
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.
Highlights
Hyperspectral remote sensing has received considerable attention over the past three decades since the development of high-altitude airborne [1,2,3] and spaceborne platforms [4], leading to a paradigm-shifting approach to Earth observation
Hyperspectral remote sensing is commonly known by its imaging modality term hyperspectral imaging (HSI) [5] and has prominent applications in fields such as geology [8,9,10], agriculture [11,12,13], forestry [14,15,16], oceanography [17,18,19], forensics [20,21,22], and ecology [23,24,25]
Since the low spatial resolution imagery was on the same scale as data products collected by satellite sensors, these results suggest that sensor blurring may be more prominent for airborne sensors due to their high spatial resolution [43]
Summary
Hyperspectral remote sensing has received considerable attention over the past three decades since the development of high-altitude airborne [1,2,3] and spaceborne platforms [4], leading to a paradigm-shifting approach to Earth observation. Contiguous narrow-band spectral information is acquired for each spatial pixel of an image collected over the Earth’s surface [5]. The spectral information typically quantifies the absorbance and reflectance of the materials within each spatial pixel, as well as the interactions that have occurred with light as it passed through the atmospheric column. Assuming the atmospheric interactions (absorption and scattering) can be reasonably well modelled and removed from the signal of each pixel [7], the spectral information from hyperspectral remote sensing data can be used to identify and characterize materials over large spatial extents. Hyperspectral remote sensing is commonly known by its imaging modality term hyperspectral imaging (HSI) [5] and has prominent applications in fields such as geology [8,9,10], agriculture [11,12,13], forestry [14,15,16], oceanography [17,18,19], forensics [20,21,22], and ecology [23,24,25]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call