Unmixing intimate mixtures using B\ue9zier surfaces

Abstract: Due to the complex interaction of incident light with intimately mixed materials, the relationship between acquired reflectance spectra and the composition of materials is highly nonlinear. Spectral variability due to changes in illumination and acquisition conditions further complicates the unmixing procedure. In this work, we propose a method to accurately characterize a nonlinear simplex by a Bezier surface, by utilizing training samples. The fractional abundances of a test sample can be estimated by reconstruction of its reflectance spectrum on the Bezier surface. Moreover, the proposed method is made invariant to changes in the acquisition and illumination conditions. Experiments are conducted on simulated and real laboratory-generated mineral powder mixtures. The experimental results confirm the potential of the proposed methodology

Soil moisture content estimation from hyperspectral remote sensing data

Abstract: Because of its significant absorption power, particularly in the SWIR optical region (e.g., absorption features around 1400 and 1900 nm), water dominates the optical reflectance properties of water-bearing materials. This allows us to study a material\u2019s water-related features, such as its moisture content from optical reflectance. In this study, we proposed a framework to estimate soil moisture content from hyperspectral remote sensing data. Validation is performed on Hyperion hyperspectral satellite imagery and ground-truth data from the Soil Moisture Network database

Mineralogical Mapping with Accurately Corrected Shortwave Infrared Hyperspectral Data Acquired Obliquely from UAVs

While uncrewed aerial vehicles are routinely used as platforms for hyperspectral sensors, their application is mostly confined to nadir imaging orientations. Oblique hyperspectral imaging has been impeded by the absence of robust registration and correction protocols, which are essential to extract accurate information. These corrections are especially important for detecting the typically small spectral features produced by minerals, and for infrared data acquired using pushbroom sensors. The complex movements of unstable platforms (such as UAVs) require rigorous geometric and radiometric corrections, especially in the rugged terrain often encountered for geological applications. In this contribution we propose a novel correction methodology, and associated toolbox, dedicated to the accurate production of hyperspectral data acquired by UAVs, without any restriction concerning view angles or target geometry. We make these codes freely available to the community, and thus hope to trigger an increasing usage of hyperspectral data in Earth sciences, and demonstrate them with the production of, to our knowledge, the first fully corrected oblique SWIR drone-survey. This covers a vertical cliff in the Dolomites (Italy), and allowed us to distinguish distinct calcitic and dolomitic carbonate units, map the qualitative abundance of clay/mica minerals, and thus characterise seismic scale facies architecture

Drone-based corrosion detection on high-voltage transmission towers using hyperspectral imaging

High-voltage transmission towers require regular inspections to identify corrosion. Traditionally, these inspections are performed through climbing, involving skilled technicians. This method is both tedious and hazardous, often necessitating the shut-down of sections of the high-voltage grid. In this paper we propose a workflow that relies on drone-based hyperspectral imaging, which enables remote assessment without endangering the technician. Currently, drones are equipped with conventional RGB cameras. However, these cameras have limited spectral resolution and range, which compromises their ability to reliably detect corrosion and often leads to false alarms. Moreover, conventional RGB cameras are unsuitable for accurately assessing the severity of corroded areas. To address these challenges, this study proposes a solution that leverages hyperspectral imaging and a dedicated processing pipeline to robustly detect corrosion and classify it based on severity level. Experiments using drones equipped with imec's VIS-NIR hyperspectral payload demonstrated the effectiveness of our developed solution

Drone-based corrosion detection on high-voltage transmission towers using hyperspectral imaging

Abstract: High-voltage transmission towers require regular inspections to identify corrosion. Traditionally, these inspections are performed through climbing, involving skilled technicians. This method is both tedious and hazardous, often necessitating the shut-down of sections of the high-voltage grid. In this paper we propose a workflow that relies on drone-based hyperspectral imaging, which enables remote assessment without endangering the technician. Currently, drones are equipped with conventional RGB cameras. However, these cameras have limited spectral resolution and range, which compromises their ability to reliably detect corrosion and often leads to false alarms. Moreover, conventional RGB cameras are unsuitable for accurately assessing the severity of corroded areas. To address these challenges, this study proposes a solution that leverages hyperspectral imaging and a dedicated processing pipeline to robustly detect corrosion and classify it based on severity level. Experiments using drones equipped with imec\u2019s VIS-NIR hyperspectral payload demonstrated the effectiveness of our developed solution

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