Abstract
Existing atmospheric correction methods retrieve surface reflectance keeping the same nominal spectral response functions (SRFs) as that of the airborne/spaceborne imaging spectrometer radiance data. Since the SRFs vary dependent on sensor type and configuration, the retrieved reflectance of the same ground object varies from sensor to sensor as well. This imposes evident limitations on data validation efforts between sensors at surface reflectance level. We propose a method to retrieve super-resolution reflectance at the surface, by combining the first-principles atmospheric correction method FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes) with spectral super-resolution of imaging spectrometer radiance data. This approach is validated by comparing airborne AVIRIS (airborne visible/infrared imaging spectrometer) and spaceborne Hyperion data. The results demonstrate that the super-resolution reflectance in spectral bands with sufficiently high signal-to-noise ratio (SNR) serves as intermediate quantity to cross validate data originating from different imaging spectrometers.
Highlights
Sensed imaging spectrometer data, called hyperspectral images in remote sensing community, need to be corrected for the effects of illumination and atmosphere to retrieve surface hemispherical-conical reflectance factor (HCRF) [1], which is most often referred to as reflectance and is the usual starting point for application analysis
The results demonstrate that the super-resolution reflectance in spectral bands with sufficiently high signal-to-noise ratio (SNR) serves as intermediate quantity to cross validate data originating from different imaging spectrometers
Combining the spectral super-resolution of hyperspectral radiance data with a superresolution improvement of FLAASH atmospheric correction method, super-resolution reflectance could be retrieved from remotely sensed images for bands with enough SNR, i.e. high incident radiation and sensor responsivity
Summary
Sensed imaging spectrometer data, called hyperspectral images in remote sensing community, need to be corrected for the effects of illumination and atmosphere to retrieve surface hemispherical-conical reflectance factor (HCRF) [1], which is most often referred to as reflectance and is the usual starting point for application analysis. There are a few commercial atmospheric correction codes widely used to retrieve surface reflectance, including ATREM (Atmospheric REMoval program) [2], FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) [3,4], ACORN (Atmospheric CORrection ) [5], ATCOR (Atmospheric/Topographic Correction) [6], and HATCH (High-accuracy Atmospheric Correction for Hyperspectral Data) [7] They all generate surface reflectance with the same number of spectral bands as the input radiance images which are bandpass sampling, called convolution, of the actual at-sensor radiance with the spectral response functions (SRFs) of the corresponding imaging spectrometer [8]. This presents a technical barrier for cross-validation of reflectance data from different sensors and for the retrieval of stable spectral fingerprints
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