Repeatability of hyperspectral imaging systems—quantification and improvement

Abstract

This paper addresses a generic problem in remote sensing by aerial hyperspectral imaging systems, that is, very low spatial and spectral repeatability of image cubes. Most analysts are either unaware of this problem or just ignore it. Hyperspectral image cubes acquired in consecutive flights over the same target should ideally be identical. In practice, two consecutive flights over the same target usually yield significant differences between the image cubes. These differences are due to variations in: target characteristics, solar illumination, atmospheric conditions and errors of the imaging system proper. Manufacturers of remote sensing imaging systems use sophisticated equipment to accurately calibrate their instruments, using optimal illumination and constant environment conditions. From a user's perspective, these calibration procedures are only of marginal interest because repeatability is ‘target dependent’. The analyst of hyperspectral imagery is primarily interested in the reliability of the end product, i.e. the repeatability of two image cubes consecutively acquired over the same target, after radiometric calibration, geo‐referencing and atmospheric corrections. Clearly, when the non‐repeatability variance is similar in magnitude to the variance of the spectral or spatial information of interest, it would be impossible to use it for classification or quantification prediction modelling. We present a simple approach for objective assessment of spatial and spectral repeatability by multiple image cube acquisitions, wherein the imaging system views a barium sulphate (BaSO₄) painted panel illuminated by a halogen lamp and by consecutive flights over a reference target. The data analysis is based on several indexes, which were developed for quantifying the spectral and spatial repeatability of hyperspectral image cubes and for detecting outlier voxels. The spectral repeatability information can be used to average less repeatable spectral bands or to exclude them from the analysis. The spatial repeatability information may be used for identifying less repeatable regions of the target. Outlier voxels should be excluded from the analysis because they are grossly erroneous data. Modus operandi for image cube acquisitions is provided, whereby the repeatability may be improved. Spatial and spectral averaging algorithms and software were developed for increasing the repeatability of image cubes in post‐processing.