Abstract
In this study, the results from a round-robin test of hyperspectral imaging systems are presented and analyzed. Fourteen different pushbroom hyperspectral systems from eight different institutions were used to acquire spectral cubes from the visible, near infra-red and short-wave infra-red regions. Each system was used to acquire a common set of targets under their normal operating conditions with the data calibrated and processed using the standard processing pipeline for each system. The test targets consisted of a spectral wavelength standard and of a custom-made pigment panel featuring Renaissance-era pigments frequently found in paintings from that period. The quality and accuracy of the resulting data was assessed with quantitative analyses of the spectral, spatial and colorimetric accuracy of the data. The results provide a valuable insight into the accuracy, reproducibility and precision of hyperspectral imaging equipment when used under routine operating conditions. The distribution and type of error found within the data can provide useful information on the fundamental and practical limits of such equipment when used for applications such as spectral classification, change detection, colorimetry and others.
Highlights
Hyperspectral imaging provides a powerful combination of high spectral resolution and dense spatial mapping and has become a valuable analytical tool in a wide range of fields, including remote sensing [1], food science [2], astronomy [3], mineralogy [4], agriculture [5], medicine [6], the study of art [7] and many others
The results presented here are from two of the test targets used for the round-robin test: a diffuse lambertian Zenith Polymer R wavelength standard and a custom-made pigment panel made up of 7 different historical pigments in a tempera binder
We will look in detail at the results obtained from the different VNIR and SWIR
Summary
Hyperspectral imaging provides a powerful combination of high spectral resolution and dense spatial mapping and has become a valuable analytical tool in a wide range of fields, including remote sensing [1], food science [2], astronomy [3], mineralogy [4], agriculture [5], medicine [6], the study of art [7] and many others. Hyperspectral data can be used in a number of ways, including, for example, materials mapping and identification, the detection of hidden features, change monitoring and many other applications. The accuracy of real-world hyperspectral imaging systems is limited by the technical characteristics of the hyperspectral cameras themselves, and by the overall setup, how they are used and how the acquired data is processed. These factors include the power and spectral content of the lighting used, the optical geometry, the integration time and acquisition parameters, as well as the kind of processing and calibration of the data that is carried out. When monitoring change, when performing classification using 3rd party spectral libraries or when combining a variety of data sets in a multimodal analysis
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