A fast smoothing algorithm for post-processing of surface reflectance spectra retrieved from airborne imaging...

Area of Science:

• Remote Sensing
• Geospatial Analysis
• Spectroscopy

Background:

• Hyperspectral imaging data often contain atmospheric and calibration artifacts.
• Radiative transfer models may not fully correct these spectral errors.

Purpose of the Study:

• To develop a fast post-processing technique for smoothing retrieved surface reflectance spectra.
• To reduce atmospheric absorption/scattering effects and calibration artifacts in hyperspectral data.

Main Methods:

• Cubic spline smoothing algorithm applied via moving filters.
• Derivation and application of a common gain curve to correct spectral artifacts.
• Analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data.

Main Results:

• Successfully smoothed surface reflectance spectra from hyperspectral data.
• Demonstrated reduction of residual atmospheric and calibration artifacts.
• Achieved comparable results to the empirical line method.

Conclusions:

• The developed spectral smoothing technique effectively enhances the quality of retrieved surface reflectance spectra.
• This method offers a fast and efficient way to improve hyperspectral data for various applications.
• The technique shows promise for refining remote sensing data analysis.