Hyperspectral Imaging Remote Sensing

2016 ◽  
Author(s):  
Dimitris Manolakis ◽  
Ronald Lockwood ◽  
Thomas Cooley
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging

scholarly journals Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2407
Author(s):  
Hojun You ◽  
Dongsu Kim
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Optical Flow ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Template Matching ◽  
Hyperspectral Images ◽  
Spatial Accuracy ◽  
Flow Algorithm ◽  
Fluvial Remote Sensing

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

Remote sensing of gases by hyperspectral imaging: results of measurements in the Hamburg port area

2011 ◽  
Author(s):  
Samer Sabbah ◽  
Peter Rusch ◽  
Jörn-Hinnrich Gerhard ◽  
Christian Stöckling ◽  
Jens Eichmann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Port Area ◽  
Imaging Results

scholarly journals Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

2018 ◽  
Vol 10 (12) ◽  
pp. 2027 ◽  
Author(s):  
Itiya Aneece ◽  
Prasad Thenkabail
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
The United States ◽  
Google Earth ◽  
Growth Stages ◽  
Agricultural Crops ◽  
Spectral Library ◽  
Crop Types ◽  
Google Earth Engine

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

scholarly journals Visualizing Polymer Damage Using Hyperspectral Imaging

Polymers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2071
Author(s):  
Monika Bleszynski ◽  
Shaun Mann ◽  
Maciej Kumosa
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Electrical Resistance ◽  
Electrical Insulation ◽  
Spectral Signature ◽  
Proof Of Concept ◽  
Industrial Materials ◽  
Ceramic Insulators ◽  
Silicone Rubbers

Silicone rubbers (SIRs) are common industrial materials which are often used for electrical insulation including weather sheds on non-ceramic insulators (NCIs). While SIRs are typically resilient to outside environments, aging can damage SIRs’ favorable properties such as hydrophobicity and electrical resistance. Detecting SIR aging and damage, however, can be difficult, especially in service. In this study we used hyperspectral imaging (HSI) and previously investigated aging methods as a proof of concept to show how HSI may be used to detect various types of aging damage in different SIR materials. The spectral signature changes in four different SIRs subjected to four different in-service aging environments all occurred between 400––650 nm. Therefore, remote sensing of NCIs using HSI could concentrate on bands below 700 nm to successfully detect in service SIR damage.

scholarly journals Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah

2019 ◽  
Vol 11 (17) ◽  
pp. 1987 ◽  
Author(s):  
Lei Sun ◽  
Shuhab Khan ◽  
Peter Shabestari
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Gold Mineralization ◽  
Pyrite Oxidation ◽  
Gold Deposits ◽  
Remote Sensing Technique ◽  
Geochemical Study ◽  
Resource Exploration ◽  
Ore Grades ◽  
Siliciclastic Rocks

The Goldstrike district in southwest Utah is similar to Carlin-type gold deposits in Nevada that are characterized by sediment-hosted disseminated gold. Suitable structural and stratigraphic conditions facilitated precipitation of gold in arsenian pyrite grains from ascending gold-bearing fluids. This study used ground-based hyperspectral imaging to study a core drilled in the Goldstrike district covering the basal Claron Formation and Callville Limestone. Spectral modeling of absorptions at 2340, 2200, and 500 nm allowed the extraction of calcite, clay minerals, and ferric iron abundances and identification of lithology. This study integrated remote sensing and geochemistry data and identified an optimum stratigraphic combination of limestone above and siliciclastic rocks below in the basal Claron Formation, as well as decarbonatization, argillization, and pyrite oxidation in the Callville Limestone, that are related with gold mineralization. This study shows an example of utilizing ground-based hyperspectral imaging in geological characterization, which can be broadly applied in the determination of mining interests and classification of ore grades. The utilization of this new terrestrial remote sensing technique has great potentials in resource exploration and exploitation.

scholarly journals Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in the Water Column of Freshwater Lakes

2020 ◽  
Vol 12 (11) ◽  
pp. 1745
Author(s):  
Michael Seidel ◽  
Christopher Hutengs ◽  
Felix Oertel ◽  
Daniel Schwefel ◽  
András Jung ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Column ◽  
Hyperspectral Imaging ◽  
Quality Parameters ◽  
Optically Active ◽  
Freshwater Lakes ◽  
Active Substances ◽  
Optically Active Substances

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS.

scholarly journals Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing

2020 ◽  
Vol 16 (11) ◽  
pp. 155014772096846
Author(s):  
Jing Liu ◽  
Yulong Qiao
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Image Classification ◽  
Hyperspectral Imaging ◽  
Mahalanobis Distance ◽  
Hyperspectral Image ◽  
Supervised Machine Learning ◽  
Practical Applications ◽  
Spectral Dimensionality

Spectral dimensionality reduction is a crucial step for hyperspectral image classification in practical applications. Dimensionality reduction has a strong influence on image classification performance with the problems of strong coupling features and high band correlation. To solve these issues, we propose the Mahalanobis distance–based kernel supervised machine learning framework for spectral dimensionality reduction. With Mahalanobis distance matrix–based dimensional reduction, the coupling relationship between features and the elimination of the scale effect are removed in low-dimensional feature space, which benefits the image classification. The experimental results show that compared with other methods, the proposed algorithm demonstrates the best accuracy and efficiency. The Mahalanobis distance–based multiples kernel learning achieves higher classification accuracy than the Euclidean distance kernel function. Accordingly, the proposed Mahalanobis distance–based kernel supervised machine learning method performs well with respect to the spectral dimensionality reduction in hyperspectral imaging remote sensing.

Hyperspectral drill-core imaging for ore characterization

2020 ◽  
Author(s):  
Laura Tusa ◽  
Mahdi Khodadadzadeh ◽  
Margret Fuchs ◽  
Richard Gloaguen ◽  
Jens Gutzmer
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Hyperspectral Imaging ◽  
Mineral Exploration ◽  
Electromagnetic Spectrum ◽  
Drill Core ◽  
Ore Characterization ◽  
Ore Types ◽  
Drill Cores

<p>Mineral exploration campaigns represent an essential step in the discovery and evaluation of ore deposits required to fulfil the global demand for raw materials. Thousands of meters of drill-cores are extracted in order to characterize a specific exploration target. Hyperspectral imaging is recently being explored in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. The method relies on the fact that minerals have different spectral responses in specific portions of the electromagnetic spectrum. Sensors covering the visible to near-infrared (VNIR) and short-wave infrared (SWIR) are commonly used to identify and estimate the relative abundance of minerals such as phyllosilicates, amphiboles, carbonates, iron oxides and hydroxides as well as sulphates (Clark, 1999). The distribution of these mineral phases can frequently be used as a proxy for the distribution of ore minerals such as sulphides. Typical core imaging systems can acquire hyperspectral data from a whole drill-core tray in a matter of seconds. Available sensors record data in several hundreds of contiguous spectral bands at spatial resolutions around 1 mm/pixel.</p><p>​​In this work, we apply a local high-resolution mineralogical analysis, such as SEM-MLA (Kern et al., 2018), for a precise and exhaustive mineral mapping of some selected small samples. We then upscale these mineralogical data acquired from thin sections to drill-core scale by integrating hyperspectral imaging and machine learning techniques. Our proposed method is composed of two main steps. In the first step, after initially co-registering the hyperspectral and high-resolution mineralogical data and making a training set, a machine learning model is trained. In the second step, we apply the learned model to obtain mineral abundance and association maps over entire drill-cores.</p><p>​​The mapping is further used for the calculation of other mineralogical parameters essential to exploration and further mining stages such as modal mineralogy, mineral association, alteration indices, metal grade estimates and hardness. The proposed methodological framework is illustrated on samples collected from a porphyry type deposit, but the procedure is easily adaptable to other ore types. Therefore, this approach can be integrated in the standard core-logging routine, complementing the on-site geologists and can serve as background for the geometallurgical analysis of numerous ore types.  </p><p>​​</p><p>​​Clark, R. N., 1999, “Spectroscopy of rocks and minerals, and principles of spectroscopy,” in Remote sensing for the earth sciences: Manual of remote sensing, vol. 3, John Wiley & Sons, Inc, pp. 3–58.</p><p>​​Gandhi, S. M. and Sarkar, B. C., 2016, “Drilling,” in Essentials of Mineral Exploration and Evaluation, pp. 199–234.</p><p>​​Kern, M., Möckel, R., Krause, J., Teichmann, J., Gutzmer, J., 2018. Calculating the deportment of a fine-grained and compositionally complex Sn skarn with a modified approach for automated mineralogy. Miner. Eng. 116, 213–225.</p>

Hyperspectral imaging Fourier transform spectrometers for astronomical and remote sensing observations

1994 ◽  
Author(s):  
Bruce Rafert ◽  
R. Glenn Sellar ◽  
Eirik Holbert ◽  
Joel H. Blatt ◽  
David W. Tyler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Hyperspectral Imaging
Sign in / Sign up

Export Citation Format

Share Document