Spectrum reconstruction using relative-deviation-based kernel regression in temporally and spatially modulated...

Area of Science:

• Spectroscopy
• Remote Sensing
• Signal Processing

Background:

• Fourier transform imaging spectrometers (FTIS) are susceptible to platform motion during scanning.
• Platform instability leads to deviations in target interferograms and inaccurate spectral recovery.
• This affects the ability to characterize target properties from aerial imagery.

Purpose of the Study:

• To develop and validate a robust method for accurate spectral recovery from FTIS data acquired during platform motion.
• To improve the characterization of target properties in aerial imaging applications.

Main Methods:

• Implemented a target tracking algorithm using kernel regression to determine target positions in image sequences.
• Utilized a relative deviation method for calculating target intensities.
• Employed the nonuniform fast Fourier transform (NUFFT) algorithm for spectrogram recovery.

Main Results:

• The proposed method effectively acquires target positions and intensities despite platform motion.
• Spectrogram recovery using NUFFT demonstrated high accuracy when compared to ideal spectrograms.
• Validation was performed on both simulated and experimental aerial image data.

Conclusions:

• The developed target tracking and spectral recovery technique significantly enhances the accuracy of FTIS data analysis.
• This method provides reliable target spectral characteristics even with unstable platform conditions in aerial remote sensing.