In this paper, we present a hyper-spectral imaging system and practical calibration procedure using a low-cost calibration reference made of polytetrafluoroethylene. The imaging system includes a hyperspectral camera and an active source of illumination with a variable spectral distribution of intensity. The calibration reference is used to measure the relative reflectance of any material surface independent of the spectral distribution of light and camera sensitivity. Winter road conditions are taken as a test application, and several spectral images of snow, icy asphalt, dry asphalt, and wet asphalt were made at different exposure times using different illumination spectra. Graphs showing measured relative reflectance for different road conditions support the conclusion that measurements are independent of illumination. Principal component analysis of the acquired spectral data for road conditions shows well separated data clusters, demonstrating the system’s suitability for material classification.

In this work, a multi-exposure method is proposed to increase the dynamic range (DR) of hyperspectral imaging using an InGaAs-based short-wave infrared (SWIR) hyperspectral line camera. Spectral signatures of materials were captured for scenarios in which the DR of a scene was greater than the DR of a line camera. To demonstrate the problem and test the proposed multi-exposure method, plastic detection in food waste and polymer sorting were chosen as the test application cases. The DR of the hyperspectral camera and the test samples were calculated experimentally. A multi-exposure method is proposed to create high-dynamic-range (HDR) images of food waste and plastic samples. Using the proposed method, the DR of SWIR imaging was increased from 43 dB to 73 dB, with the lowest allowable signal-to-noise ratio (SNR) set to 20 dB. Principal Component Analysis (PCA) was performed on both HDR and non-HDR image data from each test case to prepare the training and testing data sets. Finally, two support vector machine (SVM) classifiers were trained for each test case to compare the classification performance of the proposed multi-exposure HDR method against the single-exposure non-HDR method. The HDR method was found to outperform the non-HDR method in both test cases, with the classification accuracies of 98% and 90% respectively, for the food waste classification, and with 95% and 35% for the polymer classification.

Hyperspectral remote sensing is known to suffer from wavelength bands blocked by atmospheric gases. Short-wave infrared hyperspectral imaging at in situ installations is shown to be affected by water vapour even if the pathlength of light through air is only hundreds of centimetres. This impact is especially noticeable with large variations of relative humidity, the coefficient of variation reaching 5 % in our test case. Using repeated calibrations of imaging system at the same relative humidity as in the measurement, we were able to reduce the coefficient of variation to 1 %. The measurement variations are also shown to induce significant error in material classification. Polymer type identification was selected as the test case for material classification. The measurement variations due to the change in relative humidity are shown to result in 20 % classification error at its minimum. With repeated calibrations or by eliminating the most affected wavelength bands from measurements, we were able to reduce the classification error to less than 1 %. Such improvement of measurement and classification precision may be important for industrial applications such as waste sorting, polymer classification etc.