Calibration of a Hyper-Spectral Imaging System Using a Low-Cost Reference

Shaikh, Muhammad Saad; Jaferzadeh, Keyvan; Thörnberg, Benny; Casselgren, Johan

doi:10.3390/s21113738

Cited by 24 publications

(20 citation statements)

References 23 publications

(42 reference statements)

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“…This PTFE tile is a low-cost alternative to the conventionally used material Spectralon TM . It is known to diffusely reflect light with as even a distribution as Spectralon TM over the range of all wavelengths in near-infrared (NIR) regions [21]. This PTFE tile is also insensitive to humidity, a property of particular interest when experimenting in harsh environments like waste management facilities.…”

Section: Reference Targetmentioning

confidence: 99%

“…Any signal below the DC noise floor is not meaningful. A detailed DC estimation model for the Specim Fx17e hyperspectral camera is presented in [21].…”

Section: Dark Currentmentioning

confidence: 99%

“…The relative reflectance is defined as the wavelength-dependent fraction of reflected light intensity from the analyzed surface versus the reflected light intensity from the reference target. The exact spectral calibration procedure we applied to measure the relative reflectance of food and plastic samples is presented in [21].…”

Section: Spectral Calibrationmentioning

confidence: 99%

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Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging

Shaikh

Jaferzadeh²,

Thörnberg³

2022

Sensors

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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.

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Section: Reference Targetmentioning

confidence: 99%

“…Any signal below the DC noise floor is not meaningful. A detailed DC estimation model for the Specim Fx17e hyperspectral camera is presented in [21].…”

Section: Dark Currentmentioning

confidence: 99%

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Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging

Shaikh

Jaferzadeh²,

Thörnberg³

2022

Sensors

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“…To obtain more robust data, our material hyperspectral database is created under multiple sessions with different acquisition conditions. We applied the calibration procedure given in [15,16,17] to allow comparisons between spectra acquired in different conditions.…”

Section: Introductionmentioning

confidence: 99%

Case Study of a Calibration Problem in Acquired Hyperspectral Images

Caubet

Guerrini

Desbarats

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Hyperspectral imaging allows the classification and localization of materials for diverse applications. The existing datasets are either limited to a single image or created for specific applications. In our work, we need a dataset of urban materials for classification. However, the illumination and acquisition conditions are varying over time. This impacts the raw hyperspectral images and drives the need of making images independent from the acquisition conditions. To deal with it, classical approaches are based on the conversion of raw images to radiance and reflectance. Though many studies have been conducted on reflectance images and on ways to improve their robustness to such changes, the selection of a proper radiance image has yet to be studied. In this paper, we first describe the creation of a dataset, then study two methods for correcting the radiance computation and comment the results of the proposed process.

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“…Radiometric calibration, 9 calibration of true reflectance 10,11 or relative reflectance 12 of hyperspectral cameras, or hyperspectral imaging systems are known computational methods used to suppress impact from variations in light intensity, pixel sensitivities and environmental parameters. Despite the well-known impact of water vapour in the SWIR region of light, 2,8 we could not find any published studies of the impact of water vapour on in situ hyperspectral imaging systems and material classification.…”

Section: Introductionmentioning

confidence: 99%

Impact of water vapour on polymer classification using in situ short-wave infrared hyperspectral imaging

Shaikh

Thörnberg

2022

J. Spectral Imaging

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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.

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Calibration of a Hyper-Spectral Imaging System Using a Low-Cost Reference

Cited by 24 publications

References 23 publications

Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging

Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging

Case Study of a Calibration Problem in Acquired Hyperspectral Images

Impact of water vapour on polymer classification using in situ short-wave infrared hyperspectral imaging

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