Hyperspectral System Research Articles

VISIBLE, VERY NEAR IR AND SHORT WAVE IR HYPERSPECTRAL DRONE IMAGING SYSTEM FOR AGRICULTURE AND NATURAL WATER APPLICATIONS

Abstract. The accurate determination of the quality parameters of crops requires a spectral range from 400 nm to 2500 nm (Kawamura et al., 2010, Thenkabail et al., 2002). Presently the hyperspectral imaging systems that cover this wavelength range consist of several separate hyperspectral imagers and the system weight is from 5 to 15 kg. In addition the cost of the Short Wave Infrared (SWIR) cameras is high (~ 50 k€). VTT has previously developed compact hyperspectral imagers for drones and Cubesats for Visible and Very near Infrared (VNIR) spectral ranges (Saari et al., 2013, Mannila et al., 2013, Näsilä et al., 2016). Recently VTT has started to develop a hyperspectral imaging system that will enable imaging simultaneously in the Visible, VNIR, and SWIR spectral bands. The system can be operated from a drone, on a camera stand, or attached to a tractor. The targeted main applications of the DroneKnowledge hyperspectral system are grass, peas, and cereals. In this paper the characteristics of the built system are shortly described. The system was used for spectral measurements of wheat, several grass species and pea plants fixed to the camera mount in the test fields in Southern Finland and in the green house. The wheat, grass and pea field measurements were also carried out using the system mounted on the tractor. The work is part of the Finnish nationally funded DroneKnowledge – Towards knowledge based export of small UAS remote sensing technology project.

Hyperspectral Imaging for Real-Time Unmanned Aerial Vehicle Maritime Target Detection

This work address hyperspectral imaging systems use for maritime target detection using unmanned aerial vehicles. Specifically, by working in the creation of a hyperspectral real-time data processing system pipeline. We develop a boresight calibration method that allows to calibrate the position of the navigation sensor related to the camera imaging sensor, and improve substantially the accuracy of the target geo-reference. We also develop an unsupervised method for segmenting targets (boats) from their dominant background in real-time. We evaluated the performance of our proposed system for target detection in real-time with UAV flight data and present detection results comparing favorably our approach against other state-of- the-art method.

Oil pollution discrimination by an inelastic hyperspectral Scheimpflug lidar system.

An inelastic hyperspectral Scheimpflug lidar system is developed for range-resolved oil pollution detection and discrimination. A theory of system parametric design is built for aquatic circumstances, and laser-induced fluorescence spectra with an excitation wavelength of 446 nm are employed to detect oil pollution. Seven kinds of typical oil samples are tested and well distinguished using the principal component analysis (PCA) and linear discriminant analysis (LDA) methods. It has been shown that blue laser diodes (LD) have great potential for oil pollution detection, and our system could be further utilized for more applications in both marine and terrestrial environments.

Modeling and intercomparison of field and laboratory hyperspectral goniometer measurements with G-LiHT imagery of the Algodones Dunes

We compare field hyperspectral bidirectional reflectance distribution function (BRDF) measurements acquired by a hyperspectral goniometer system known as the goniometer of the Rochester Institute of Technology (GRIT) during an experiment in the Algodones Dunes system in March 2015 with NASA Goddard’s light detection and ranging, hyperspectral, and thermal imagery of the site acquired during the experiment. We augment our field spectral data collection with laboratory hyperspectral BRDF measurements of samples brought back from the Algodones Dunes site using GRIT and our second-generation goniometer GRIT-two (GRIT-T). In these laboratory experiments, we vary geophysical parameters such as sediment density and grain size distribution of the sediments that would typically impact observed BRDF with the goal of extending the range of applicability of our resulting BRDF spectral libraries. Geotechnical measurements on site confirm the variability of geophysical parameters such as density and grain size distributions within the dune system, and measurements with GRIT and GRIT-T demonstrate the impact on observed spectral variation. By augmenting field spectral libraries with laboratory BRDF, we show that a greater proportion of the dune system is more faithfully represented in the expanded spectral library. Beyond developing appropriate calibration data for airborne and satellite imagery of the Algodones Dunes, laboratory and field studies also support goals to develop reliable retrieval methods for geophysical quantities such as sediment density directly from spectral imagery. We consider approaches based on the Hapke model. Our approaches use the invariance of the observed functional forms of the single scattering phase function, which must be invariant to differences in the illumination geometry. Fill factor is retrieved and correlates with expected direct measurements of sediment density in a laboratory setting.

PIXEL-BASED LAND COVER CLASSIFICATION BY FUSING HYPERSPECTRAL AND LIDAR DATA

Abstract. Land cover classification has many applications like forest management, urban planning, land use change identification and environment change analysis. The passive sensing of hyperspectral systems can be effective in describing the phenomenology of the observed area over hundreds of (narrow) spectral bands. On the other hand, the active sensing of LiDAR (Light Detection and Ranging) systems can be exploited for characterising topographical information of the area. As a result, the joint use of hyperspectral and LiDAR data provides a source of complementary information, which can greatly assist in the classification of complex classes. In this study, we fuse hyperspectral and LiDAR data for land cover classification. We do a pixel-wise classification on a disjoint set of training and testing samples for five different classes. We propose a new feature combination by fusing features from both hyperspectral and LiDAR, which achieves competent classification accuracy with low feature dimension, while the existing method requires high dimensional feature vector to achieve similar classification result. Also, for the reduction of the dimension of the feature vector, Principal Component Analysis (PCA) is used as it captures the variance of the samples with a limited number of Principal Components (PCs). We tested our classification method using PCA applied on hyperspectral bands only and combined hyperspectral and LiDAR features. Classification with support vector machine (SVM) and decision tree shows that our feature combination achieves better classification accuracy compared to the existing feature combination, while keeping the similar number of PCs. The experimental results also show that decision tree performs better than SVM and requires less execution time.

Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors

Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure.

Detection of pits in fresh and frozen cherries using a hyperspectral system in transmittance mode

The aim of this study was to develop an improved method for detecting internal intrusions (pits and their fragments) in cherries using hyperspectral imaging technique in transmittance mode. Hyperspectral transmission images of pitted and intact cherries of three popular cultivars: ‘Łutówka’, ‘Pandy 103’, and ‘Groniasta’, differing by soluble solid content (SSC), were acquired in the visible and near-infrared (VNIR) range (450–1000 nm). The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the hyperspectral data were used to construct the supervised classification models. From all the studied classifiers, the best prediction accuracies for whole pit or pit fragment detection were obtained by the backpropagation neural network (BNN) model (94.6% of correctly classified instances for fresh samples and 83.3% for frozen samples). The accuracy of distinguishing between drilled and intact cherries was higher than 87% jointly for fresh and frozen cherries. These results showed that hyperspectral imaging in transmittance mode is an accurate and objective tool for pit detection in fresh and frozen cherries and can be applied in on-line sorting systems.

A Satellite-Based Imaging Instrumentation Concept for Hyperspectral Thermal Remote Sensing.

This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1–5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2–12.5 µm (instrument NEDT 0.05 K–0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0–10.25 µm and 10.25–12.5 µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1–3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval.

Effects of topical negative pressure therapy on tissue oxygenation and wound healing in vascular foot wounds

ObjectiveTopical negative pressure (TNP) therapy is widely used in the treatment of acute wounds in vascular patients on the basis of proposed multifactorial benefits. However, numerous recent systematic reviews have concluded that there is inadequate evidence to support its benefits at a scientific level. This study evaluated the changes in wound volume, surface area, depth, collagen deposition, and tissue oxygenation when using TNP therapy compared with traditional dressings in patients with acute high-risk foot wounds. MethodsThis study was performed with hospitalized vascular patients. Forty-eight patients were selected with an acute lower extremity wound after surgical débridement or minor amputation that had an adequate blood supply without requiring further surgical revascularization and were deemed suitable for TNP therapy. The 22 patients who completed the study were randomly allocated to a treatment group receiving TNP or to a control group receiving regular topical dressings. Wound volume and wound oxygenation were analyzed using a modern stereophotographic wound measurement system and a hyperspectral transcutaneous oxygenation measurement system, respectively. Laboratory analysis was conducted on wound biopsy samples to determine hydroxyproline levels, a surrogate marker to collagen. ResultsDifferences in clinical or demographic characteristics or in the location of the foot wounds were not significant between the two groups. All patients, with the exception of two, had diabetes. The two patients who did not have diabetes had end-stage renal failure. There was no significance in the primary outcome of wound volume reduction between TNP and control patients on day 14 (44.2% and 20.9%, respectively; P = .15). Analyses of secondary outcomes showed a significant result of better healing rates in the TNP group by demonstrating a reduction in maximum wound depth at day 14 (36.0% TNP vs 17.6% control; P = .03). No significant findings were found for the other outcomes of changes in hydroxyproline levels (58.0% TNP vs 94.5% control; P = .32) or tissue perfusion by tissue oxyhemoglobin saturation (19.4% TNP vs 12.0% control; P = .07) at day 14. At 1 year of follow-up, there were no significant outcomes in the analysis of wound failure, major amputation, and overall survival rates between the two groups. ConclusionsIn this pilot study, applying TNP to acute high-risk foot wounds in patients with diabetes or end-stage renal failure improved the wound healing rate in reference to wound depth. This suggests that TNP may play a role in enhancing wound healing. This study sets the foundation for larger studies to evaluate the superiority of TNP over traditional dressings in high-risk foot wounds.

Regional Retrieval Processor for Direct Broadcast High-Resolution Infrared Data

AbstractThe Space Science Engineering Center, in collaboration with the Mauna Kea Weather Center at the University of Hawai’i at Mānoa, has developed a regional retrieval processor for high-spectral-resolution infrared data. The core of the processor makes use of an inversion system, referred to as Mirto, which combines, in a Bayesian way, the a priori knowledge of the atmospheric state, based on available numerical weather prediction forecasts, with the physical information embedded in satellite observations. Forecast temperature and water vapor mixing ratio fields over the central North Pacific Ocean are adjusted to produce synthetic radiances closer and closer to the Suomi NPP Cross-track Infrared Sounder (CrIS) observations taken in clear-sky conditions. The paucity of synoptic observations over this area and the highly homogeneous background represented by the ocean provide a good framework for the implementation of this hyperspectral data inversion system. Nearly real-time (less than 60 min from overpass time) Internet publication of retrieved atmospheric profiles is made possible by the availability of a direct broadcast system that provides data from the Suomi NPP platform (CrIS and VIIRS). The main goal for the implemented system is to provide the forecasting community with products suitable for nowcasting applications and for optimal data assimilation. The implemented processor has been running routinely since August 2013. Validation based on the comparisons of retrievals with rawinsonde data from Hilo, Hawaii, and Lihue, Hawaii, and GPS-derived total precipitable water from four stations, performed over a time period of more than 1 year, shows a statistically significant improvement on the background atmospheric state used as a priori information.

Effective detection of benzoyl peroxide in flour based on parameter selection of Raman hyperspectral system

ABSTRACTPenetration depth and spatial resolution of Raman hyperspectral imaging system were studied for effective detection of benzoyl peroxide in flour. The determinations of parameters were achieved by using the single-band background-correct image of a benzoyl peroxide Raman characteristic band and a simple threshold method. The selected parameters were used to detect mixture samples with different concentrations. Percentage of detected benzoyl peroxide pixels was positively correlated to its concentration. The result shows that parameters selected in this study are effective for the detection of benzoyl peroxide additive in flour and can be used for quantitative analysis in the future.

Characterization of Fine Metal Particles Derived from Shredded WEEE Using a Hyperspectral Image System: Preliminary Results.

Waste of electric and electronic equipment (WEEE) is the fastest-growing waste stream in Europe. The large amount of electric and electronic products introduced every year in the market makes WEEE disposal a relevant problem. On the other hand, the high abundance of key metals included in WEEE has increased the industrial interest in WEEE recycling. However, the high variability of materials used to produce electric and electronic equipment makes key metals’ recovery a complex task: the separation process requires flexible systems, which are not currently implemented in recycling plants. In this context, hyperspectral sensors and imaging systems represent a suitable technology to improve WEEE recycling rates and the quality of the output products. This work introduces the preliminary tests using a hyperspectral system, integrated in an automatic WEEE recycling pilot plant, for the characterization of mixtures of fine particles derived from WEEE shredding. Several combinations of classification algorithms and techniques for signal enhancement of reflectance spectra were implemented and compared. The methodology introduced in this study has shown characterization accuracies greater than 95%.

Surface Reflectance and Sun-Induced Fluorescence Spectroscopy Measurements Using a Small Hyperspectral UAS

This study describes the development of a small hyperspectral Unmanned Aircraft System (HyUAS) for measuring Visible and Near-Infrared (VNIR) surface reflectance and sun-induced fluorescence, co-registered with high-resolution RGB imagery, to support field spectroscopy surveys and calibration and validation of remote sensing products. The system, namely HyUAS, is based on a multirotor platform equipped with a cost-effective payload composed of a VNIR non-imaging spectrometer and an RGB camera. The spectrometer is connected to a custom entrance optics receptor developed to tune the instrument field-of-view and to obtain systematic measurements of instrument dark-current. The geometric, radiometric and spectral characteristics of the instruments were characterized and calibrated through dedicated laboratory tests. The overall accuracy of HyUAS data was evaluated during a flight campaign in which surface reflectance was compared with ground-based reference measurements. HyUAS data were used to estimate spectral indices and far-red fluorescence for different land covers. RGB images were processed as a high-resolution 3D surface model using structure from motion algorithms. The spectral measurements were accurately geo-located and projected on the digital surface model. The overall results show that: (i) rigorous calibration enabled radiance and reflectance spectra from HyUAS with RRMSE < 10% compared with ground measurements; (ii) the low-flying UAS setup allows retrieving fluorescence in absolute units; (iii) the accurate geo-location of spectra on the digital surface model greatly improves the overall interpretation of reflectance and fluorescence data. In general, the HyUAS was demonstrated to be a reliable system for supporting high-resolution field spectroscopy surveys allowing one to collect systematic measurements at very detailed spatial resolution with a valuable potential for vegetation monitoring studies. Furthermore, it can be considered a useful tool for collecting spatially-distributed observations of reflectance and fluorescence that can be further used for calibration and validation activities of airborne and satellite optical images in the context of the upcoming FLEX mission and the VNIR spectral bands of optical Earth observation missions (i.e., Landsat, Sentinel-2 and Sentinel-3).

Autonomous marine hyperspectral radiometers for determining solar irradiances and aerosol optical properties

Abstract. We have developed two hyperspectral radiometer systems which require no moving parts, shade rings or motorised tracking, making them ideally suited for autonomous use in the inhospitable remote marine environment. Both systems are able to measure direct and diffuse hyperspectral irradiance in the wavelength range 350–1050 nm at 6 nm (Spectrometer 1) or 3.5 nm (Spectrometer 2) resolution. Marine field trials along a 100° transect (between 50° N and 50° S) of the Atlantic Ocean resulted in close agreement with existing commercially available instruments in measuring (1) photosynthetically available radiation (PAR), with both spectrometers giving regression slopes close to unity (Spectrometer 1: 0.960; Spectrometer 2: 1.006) and R2 ∼ 0.96; (2) irradiant energy, with R2 ∼ 0.98 and a regression slope of 0.75 which can be accounted for by the difference in wavelength integration range; and (3) hyperspectral irradiance where the agreement on average was between 2 and 5 %. Two long duration land-based field campaigns of up to 18 months allowed both spectrometers to be well calibrated. This was also invaluable for empirically correcting for the wider field of view (FOV) of the spectrometers in comparison with the current generation of sun photometers ( ∼ 7.5° compared with ∼ 1°). The need for this correction was also confirmed and independently quantified by atmospheric radiative transfer modelling and found to be a function of aerosol optical depth (AOD) and solar zenith angle. Once Spectrometer 2 was well calibrated and the FOV effect corrected for, the RMSE in retrievals of AOD when compared with a CIMEL sun photometer were reduced to ∼ 0.02–0.03 with R2 > 0.95 at wavelengths 440, 500, 670 and 870 nm. Corrections for the FOV as well as ship motion were applied to the data from the marine field trials. This resulted in AOD500 nm ranging between 0.05 in the clear background marine aerosol regions and ∼ 0.5 within the Saharan dust plume. The RMSE between the handheld Microtops sun photometer and Spectrometer 2 was between 0.047 and 0.057 with R2 > 0.94.

Espectrómetros de imagen Offner

A hyperspectral sensor or imaging spectrometer is a device that collects spatial and spectral information in hundreds of contiguous and closely spectral bands. Hyperspectral imagers have many applications in several areas such as agriculture, geology, oceanography, forestry, medicine, colorimetry, security, military and others. Most of the existing hyperspectral systems were developed considering satellite or high altitude flying aircrafts applications. However, the specific development of these technologies allows their use in many other less expensive applications. In this context, the work presented in this thesis allows building high quality and low cost Offner imaging spectrometers. Rapid and efficient methods were used for the design, assembly and characterization of this type of spectrometers. Two laboratory setups developed from analytical model-based designs have been assembled showing high spectral and spatial resolutions and low distortions without specific aberrations corrected gratings nor non-spherical elements.

Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee

Due to the differences in terms of both price and quality, the availability of effective instrumentation to discriminate between Arabica and Robusta coffee is extremely important. To this aim, the use of multispectral imaging systems could provide reliable and accurate real-time monitoring at relatively low costs. However, in practice the implementation of multispectral imaging systems is not straightforward: the present work investigates this issue, starting from the outcome of variable selection performed using a hyperspectral system. Multispectral data were simulated considering four commercially available filters matching the selected spectral regions, and used to calculate multivariate classification models with Partial Least Squares-Discriminant Analysis (PLS-DA) and sparse PLS-DA. Proper strategies for the definition of the training set and the selection of the most effective combinations of spectral channels led to satisfactory classification performances (100% classification efficiency in prediction of the test set).

Applications of Imaging Spectrometry in Inland Water Quality Monitoring—a Review of Recent Developments

Inland waters represent complex and highly variable ecosystems, which are also of immense recreational and economic values to humans. The maintenance of high quality of inland water status necessitates development of means for rapid quality monitoring. Imaging spectrometry techniques are proven technology that can provide useful information for the estimation of inland water quality attributes due to fast speed, noninvasiveness, ease of use, and in situ operation. Although there have been many studies conducted on the use of imaging spectrometry for marine water quality monitoring and assessment, relatively few studies have considered inland water bodies. The aim of this review is to present an overview of imaging spectrometry technologies for the monitoring of inland waters including spaceborne and airborne and field or ground-based hyperspectral systems. Some viewpoints on the current situation and suggestions for future research directions are also proposed.

HyperDB: a hyperspectral land class database designed for an image processing system

Hyperspectral remote sensing is becoming more and more important amongst remote sensing techniques. In this paper, we present a hyperspectral database (HyperDB) designed to cooperate with an embedded hyperspectral image processing system developed by the authors. Hyperspectral data are recognized and categorized by their land coverage class and band information, and can be imported from various sources such as airborne and spaceborne sensors carried by airplanes or satellites, as well as handhold instruments based on in situ ground observations. Spectral library files can be easily stored, indexed, viewed, and exported. Since HyperDB follows standard design principles-independence, data safety, and compatibility-it satisfies the practical demand for managing categorized hyperspectral data, and can be readily expanded to other peripheral applications.

Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

Hyperspectral photoacoustic spectroscopy of highly-absorbing samples for diagnostic ocular imaging applications

ABSTRACTPhotoacoustic spectroscopy has been used to measure optical absorption coefficient and the application of tens of wavelength bands in photoacoustic spectroscopy was reported. Using optical methods, absorption-related information is, generally, derived from reflectance or transmittance values. Hence measurement accuracy is limited for highly absorbing samples where the reflectance or transmittance is too low to give reasonable signal-to-noise ratio. In this context, this paper proposes and illustrates a hyperspectral photoacoustic spectroscopy system to measure the absorption-related properties of highly absorbing samples directly. The normalized optical absorption coefficient spectrum of the highly absorbing iris is acquired using an optical absorption coefficient standard. The proposed concepts and the feasibility of the developed diagnostic medical imaging system are demonstrated using fluorescent microsphere suspensions and porcine eyes as test samples.