Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging

This study presents a novel method for the detection and quantification of atmospheric corrosion products on carbon steel. Using hyperspectral imaging (HSI) in the short-wave infrared range (SWIR) (900–1700 nm), we are able to identify the most common corrosion minerals such as: α-FeO(OH) (goethite), γ-FeO(OH) (lepidocrocite), and γ-Fe(2)O(3) (maghemite). Six carbon steel samples were artificially corroded in a salt spray chamber, each sample with a different duration (between 1 h and 120 hours). These samples were analysed by scanning X-ray diffraction (XRD) and also using a SWIR HSI system. The XRD data is used as baseline data. A random forest regression algorithm is used for training on the combined XRD and HSI data set. Using the trained model, we can predict the abundance map based on the HSI images alone. Several image correlation metrics are used to assess the similarity between the original XRD images and the HSI images. The overall abundance is also calculated and compared for XRD and HSI images. The analysis results show that we are able to obtain visually similar images, with error rates ranging from 3.27 to 13.37%. This suggests that hyperspectral imaging could be a viable tool for the study of corrosion minerals

Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging.

This study presents a novel method for the detection and quantification of atmospheric corrosion products on carbon steel. Using hyperspectral imaging (HSI) in the short-wave infrared range (SWIR) (900-1700 nm), we are able to identify the most common corrosion minerals such as: α FeO(OH) (goethite), γ FeO(OH) (lepidocrocite), and γ Fe2O3 (maghemite). Six carbon steel samples were artificially corroded in a salt spray chamber, each sample with a different duration (between 1h and 120 hours). These samples were analysed by scanning X-ray diffraction (XRD) and also using a SWIR HSI sytem. The XRD data is used as baseline data. A random forest regression algorithm is used for training on the combined XRD and HSI data set. Using the trained model, we can predict the abundance map based on the HSI images alone. Several image correlation metrics are used to assess the similarity between the original XRD images and the HSI images. The overall abundance is also calculated and compared for XRD and HSI images. The analysis results show that we are able to obtain visually similar images, with error rates ranging from 3.27 to 13.37 %. This suggests that hyperspectral imaging could be a viable tool for the study of corrosion minerals

An extensive multisensor hyperspectral benchmark datasets of intimate mixtures of mineral powders

Abstract: Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain.In this work, we generated a comprehensive hyperspectral dataset of intimate mineral powder mixtures by homogeneously mixing five different clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide). In total 325 samples were prepared. Among the 325 samples, 60 mixtures were binary, 150 were ternary, 100 were quaternary, and 15 were quinary. For each mixture (and pure clay powder), reflectance spectra are acquired by 13 different sensors, with a broad wavelength range between the visible and the long-wavelength infrared regions (i.e., between 350 nm and 15385 nm) and with a large variation in sensor types, platforms, and acquisition conditions. We will make this dataset public, to be used by the community for the validation of nonlinear unmixing methodologies (https://github.com/VisionlabUA/Multisensor_datasets

A multisensor hyperspectral benchmark dataset for unmixing of intimate mixtures

Abstract: Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain. In this work, we generated a comprehensive laboratory ground truth dataset of intimately mixed mineral powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60 binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen different hyperspectral sensors have been used to acquire the reflectance spectra of these mixtures in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm. Overlaps in wavelength regions due to the operational ranges of each sensor and variations in acquisition conditions resulted in a large amount of spectral variability. Ground truth composition is given by construction, but to verify that the generated samples are sufficiently homogeneous, XRD and XRF elemental analysis is performed. We believe these data will be beneficial for validating advanced methods for nonlinear unmixing and material composition estimation, including studying spectral variability and training supervised unmixing approaches. The datasets can be downloaded from the following link: https://github.com/VisionlabHyperspectral/Multisensor_datasets