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Image Correction and In Situ Spectral Calibration for Low-Cost, Smartphone Hyperspectral Imaging

Matthew A. Davies, Mary B. Stuart, Matthew J. Hobbs, A. J. S. McGonigle, Jon R. Willmott

2022Remote Sensing18 citationsDOIOpen Access PDF

Abstract

Developments in the portability of low-cost hyperspectral imaging instruments translate to significant benefits to agricultural industries and environmental monitoring applications. These advances can be further explicated by removing the need for complex post-processing and calibration. We propose a method for substantially increasing the utility of portable hyperspectral imaging. Vertical and horizontal spatial distortions introduced into images by ‘operator shake’ are corrected by an in-scene reference card with two spatial references. In situ light-source-independent spectral calibration is performed. This is achieved by a comparison of the ground-truth spectral reflectance of an in-scene red–green–blue target to the uncalibrated output of the hyperspectral data. Finally, bias introduced into the hyperspectral images due to the non-flat spectral output of the illumination is removed. This allows for low-skilled operation of a truly handheld, low-cost hyperspectral imager for agriculture, environmental monitoring, or other visible hyperspectral imaging applications.

Topics & Concepts

Hyperspectral imagingRemote sensingComputer scienceCalibrationFull spectral imagingComputer visionSoftware portabilityArtificial intelligenceEnvironmental scienceGeologyMathematicsStatisticsProgramming languageRemote Sensing in AgricultureRemote-Sensing Image ClassificationColor Science and Applications