Hyperspectral Analysis Research Articles

Hyperspectral analysis in areas of Caatinga degraded in the municipality of Sertânia - PE

The brazilian region semiarid has as predominant vegetation the caatinga. The Caatinga Forest has one of its striking features its excellent adaptation to arid conditions. This exclusively brazilian ecosystem has a great biodiversity, but promote their preservation is a major obstacle to be overcome, one is the best knowledge of their chemical and physical processes. Thus the hyperspectral remote sensing can help enough in the characterization and monitoring of this ecosystem, where the hyperspectral data collected by these sensors can be transformed into different information biophysical aspects of vegetable toppings. In this work was used images hyperspectral of the Hyperion Satellite Earth Observing1 sensor (EO-1). The software utilized for analysis and the pre-processing of the image was the ENVI 4.4 software, where we generated the NDVI - vegetation index (Normalized Difference Vegetation Index) and the indexes of leaf pigments PRI (Photochemical Reflectance Index), SIPI (Structure Insensitive pigment Index) and CRI (1 Carotenoid Reflectance Index).The indices generated answered well to the structure of the cover and characteristics vegetation. Allowing this way, get more accurate data of vegetation indices and leaf pigments such, as chlorophyll and carotenoids.

Characterization of the biosolids composting process by hyperspectral analysis

Composted biosolids are widely used as a soil supplement to improve soil quality. However, the application of immature or unstable compost can cause the opposite effect. To date, compost maturation determination is time consuming and cannot be done at the composting site. Hyperspectral spectroscopy was suggested as a simple tool for assessing compost maturity and quality. Nevertheless, there is still a gap in knowledge regarding several compost maturation characteristics, such as dissolved organic carbon, NO3, and NH4 contents. In addition, this approach has not yet been tested on a sample at its natural water content. Therefore, in the current study, hyperspectral analysis was employed in order to characterize the biosolids composting process as a function of composting time. This goal was achieved by correlating the reflectance spectra in the range of 400–2400nm, using the partial least squares-regression (PLS-R) model, with the chemical properties of wet and oven-dried biosolid samples. The results showed that the proposed method can be used as a reliable means to evaluate compost maturity and stability. Specifically, the PLS-R model was found to be an adequate tool to evaluate the biosolids’ total carbon and dissolved organic carbon, total nitrogen and dissolved nitrogen, and nitrate content, as well as the absorbance ratio of 254/365nm (E2/E3) and C/N ratios in the dry and wet samples. It failed, however, to predict the ammonium content in the dry samples since the ammonium evaporated during the drying process. It was found that in contrast to what is commonly assumed, the spectral analysis of the wet samples can also be successfully used to build a model for predicting the biosolids’ compost maturity.

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer

The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network’s modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific.

Local Prototype Space-based Band Selection for Hyperspectral Subpixel Analysis

Local Prototype Space-based Band Selection for Hyperspectral Subpixel Analysis

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method.

The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]′), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal.

Hyperspectral Data Analysis Algorithm Based on Partial Least-squares Regression Model and Its Application in the Identification of Hogwash Oil

Hyperspectral Data Analysis Algorithm Based on Partial Least-squares Regression Model and Its Application in the Identification of Hogwash Oil

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Mazimization-Complementary Superiority Method

Abstract. The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance and TN based on spectral reflectance curves of soil samples collected from subsided land determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]'), (correlation coefficients, P &lt; 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided land caused by the extraction of natural resources including groundwater, oil and coal.

Screening of anthocyanins in single red grapes using a non-destructive method based on the near infrared hyperspectral technology and chemometrics.

Anthocyanins are the main compounds responsible for the colour of red wines and therefore it may be important to evaluate the content of the aforesaid secondary metabolites during grape ripening due to the crucial importance to determine wine colour. Nowadays, there is a growing demand of rapid and non-destructive analytical tools for analysing grapes, such as the emerging hyperspectral analysis. The hyperspectral images of intact grapes (Vitis vinifera L. cv. Tempranillo, Graciano, Garnacha and Mazuelo red grape from vineyards located in the D.O.Ca. Rioja at two different developmental stages) were recorded using a near infrared hyperspectral imaging device (900-1700 nm). Reference values of anthocyanins were obtained by HPLC-DAD. Calibrations were performed by modified partial least squares regression and present a good potential (coefficient of determination of 0.72 and standard error of cross-validation values of 0.78 and 0.70 mg per grape for total and non-acylated anthocyanins respectively). The procedure reported here presents a good potential for a fast and reasonably inexpensive screening of these compounds in intact single berries. Moreover, the heterogeneity of anthocyanins within the same ripeness stage could be evaluated using this non-detructive tool.

Vascular Collateralization Investigated in the Chorio‐Allantoic Membrane (CAM)

IntroductionCollateralization upon obstruction of an arterial vessel is crucial to maintain tissue perfusion. The aim of this study is to quantify the dynamics of structural adaptation on the microvascular level in a model where vascular reactions can be observed continuously.MethodWhite leghorn chicken eggs were transferred on incubation day E3 into petri dishes. On E12, a selected arteriole of the CAM was occluded by laser irradiation. At several time points before and after occlusion, the area perfused by the arteriole was scanned using video microscopy. Blood flow velocity in selected vessels in the recorded area was measured (spatial correlation) and hemoglobin oxygen saturation was determined (hyperspectral analysis).Results and ConclusionBefore occlusion, the occluded arterioles were 64.86 ± 6.33 µm in diameter and microvasculature displayed an interdigitated pattern of arterioles and venules. Flow velocities exhibited a strong linear decline with decreasing vessel diameter. After the occlusion, functional collaterals were typically observed either between one daughter arteriole and an adjacent arteriole not fed by the obstructed vessel or between one daughter and the upstream sister arteriole. They developed from pre‐existing terminal arterioles (3 of 20) and/or by remodeling of capillary meshes (17 of 20). Collateralization resulted in reversal of flow in arterioles downstream of the occlusion. It is concluded that in the CAM model, collateralization maintains local microcirculatory function via pre‐existing arterial anastomoses vessels or via capillary meshes.Supported by EU / ITN “REVAMMAD” # 316990

Differentiation of Breast Lesions by Use of HyperSPACE: Hyper-Spectral Analysis for Characterization in Echography

Early diagnosis represents the cornerstone in breast cancer control. Ultrasound is still a valid tool because of its low invasiveness, reduced costs and reduced risk of harm, but better exploitation of its potential is necessary to extract information on tissue features. The proposed method, HyperSPACE (hyper-spectral analysis for characterization in echography), which processes the ultrasonic radiofrequency signal in an N-dimension spectral hyperspace to define several characteristic parameters of the tissue under investigation, was used with the aim of differentiating two types of breast lesion: infiltrating ductal carcinoma and fibroadenoma. The analyzed data set consisted of 2000 radiofrequency frames related to 200 sections of pathologic breast nodules: 104 infiltrating ductal carcinomas and 96 fibroadenomas. The algorithm was trained on single radiofrequency frames related to 50 sections (26 carcinomas, 24 fibroadenomas) to recognize the two pathologies considered, and all the radiofrequency frames related to the other 150 sections were classified, yielding a sensitivity of 92.2%, specificity of 93%, positive predictive value of 93.2% and negative predictive value of 91%. The results were compared with those of RULES (radiofrequency ultrasonic local estimators), a processing method set developed by our group and used by other researchers in clinical and laboratory environments.

Evaluation of Earth Observing-1 (EO1) Data for Lithological and Hydrothermal Alteration Mapping: A Case Study from Urumieh-Dokhtar Volcanic Belt, SE Iran

This study evaluates the capability of Advanced Land Imager (ALI) and Hyperion Earth Observing-1 (EO1) data for lithological and hydrothermal alteration mapping. Several image processing methods were applied to ALI and Hyperion images covering the Meiduk and Sar Cheshmeh porphyry copper mining districts, SE Iran. Feature-oriented principal components selection, developed band ratio and minimum noise fraction methods were able to yield spectral information for enhancing the hydrothermally altered rocks and lithological units at regional scale using ALI data. Mixture-tuned matched-filtering method discriminated hydrothermal alteration areas from surrounding rock units using shortwave infrared bands of ALI. Analytical imaging and geophysics (AIG)-developed hyperspectral analysis processing methods were applied to the visible and near infrared and shortwave infrared bands of Hyperion for mapping iron oxide/hydroxide minerals and clay mineral assemblages at district scale. Supergene altered areas consisting of iron oxide/hydroxide minerals such as hematite, limonite, jarosite and goethite were detected using visible and near infrared bands of Hyperion. Phyllic, advanced argillic and propylitic alteration zones associated with porphyry copper mineralization were discriminated using shortwave infrared bands of Hyperion. The achievements of this investigation indicate considerable implications for geologists to use Earth Observing-1 data for porphyry copper and epithermal gold exploration.

Citrus black spot detection using hyperspectral imaging

Abstract: This paper describes the development of a hyperspectral imaging approach for identifying fruits infected with citrus black spot (CBS). Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot, wind scar, or melanose. Spectral angle mapper (SAM) and spectral information divergence (SID) hyperspectral analysis approaches were used to classify fruit samples into two classes: CBS or non-CBS. The classification accuracy for CBS with SAM approach was 97.90%, and 97.14% with SID. The combination of hyperspectral images and two classification approaches (SID and SAM) have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel conditions. The study result can be a reference for the non-destructive detection of fruits infected with citrus black spot. Keywords: citrus black spot, hyperspectral imaging, spectral angle mapper, spectral information divergence, imaging processing DOI: 10.3965/j.ijabe.20140706.004 Citation: Kim D, Burks T F, Ritenour M A, Qin J W. Citrus black spot detection using hyperspectral imaging. Int J Agric & Biol Eng, 2014; 7(6): 20－27.

Hydrothermal alteration mapping from Landsat-8 data, Sar Cheshmeh copper mining district, south-eastern Islamic Republic of Iran

We studied the applicability of data from the recently launched Landsat-8 for mapping hydrothermal alteration areas and lithological units associated with porphyry copper exploration in arid and semi-arid regions. Sar Cheshmeh copper mining district in the Urumieh-Dokhtar volcanic belt in south-eastern Iran was selected for a case study. Several red–green–blue colour combination images and specialized band ratios were prepared from Landsat-8 bands. Band ratios derived from image spectra (4/2, 6/7, 5 and 10 in red–green–blue) allow identification of altered rocks, lithological units and vegetation at regional scale. Analytical imaging and geophysical hyperspectral analysis processing methods and mixture tuned matched filtering were applied to Landsat-8 bands to identify alteration zones associated with known porphyry copper deposits. Fieldwork, previous remote sensing studies and laboratory analysis were used to verify the image processing results. We conclude that Landsat-8 bands, especially bands 2 and 4 in the visible and near-infrared, 6 and 7 in the shortwave infrared and 10 in the thermal infrared spectra, contain useful information for porphyry copper exploration. The thermal infrared bands of Landsat-8 significantly improved the quality and availability of remote-sensing data for lithological mapping. The results of this investigation should encourage geologists to use Landsat-8 operational land imager and thermal infrared sensor data for porphyry copper exploration and geological purposes.

Band selection for target detection in hyperspectral imagery using sparse CEM

Band selection (BS) has been one of the hottest issues in the field of hyperspectral data analysis. Among the existing BS techniques, most of them are designed for classification and few of them are for target detection. In this article, we present a BS method designed specifically for target detection using sparse constrained energy minimization (CEM), named sparse constrained band selection (SCBS). SCBS is a supervised BS method embedded in CEM, which regularizes the CEM operator with a sparse constraint. Then, the sparse model is converted into a standard convex quadratic programming model where the global optimal solution can be obtained conveniently. The validation experiments show that SCBS is superior to the other compared BS methods in terms of target detection.

Modern approaches to processing large hyperspectral and multispectral aerospace data flows

We consider approaches to processing large hyperspectral and multispectral imaging flows produced in aerospace monitoring for solving a wide range of problems of management of natural resources, environmental security, prevention of natural disasters and technogenic accidents, as well as problems of real economy, and basic and applied sciences. We analyze the specific features of the phases of hyperspectral data analysis and describe a software and hardware system that uses new and improved methods and algorithms for processing large flows of hyperspectral and other aerospace data and has a high-performance computer. This system contains different types of software for identifying the types of given objects by solving inverse problems of remote sensing as well as by analyzing their qualitative and quantitative characteristics, combined multiparameter processing of hyperspectral aerospace data, tracking the local changes including those related to changes in meteorological conditions and vegetation periods, detecting and identifying the types of small objects on the basis of analysis of individual parts of the image, detecting and identifying heat sources, etc. We bring examples of processing of hyperspectral and multispectral satellite images with the help of software and hardware tools developed.

Spectral unmixing via data-guided sparsity.

Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization, and understanding. From an unsupervised learning perspective, this problem is very challenging-both the spectral bases and their composite percentages are unknown, making the solution space too large. To reduce the solution space, priors. In practice, these priors would easily lead to some unsuitable solution. This is because they are achieved by applying an identical strength of constraints to all the factors, which does not hold in practice. To overcome this limitation, we propose a novel sparsity-based method by learning a data-guided map (DgMap) to describe the individual mixed level of each pixel. Through this DgMap, the l(p) (0 < p < 1) constraint is applied in an adaptive manner. Such implementation not only meets the practical situation, but also guides the spectral bases toward the pixels under highly sparse constraint. What is more, an elegant optimization scheme as well as its convergence proof have been provided in this paper. Extensive experiments on several datasets also demonstrate that the DgMap is feasible, and high quality unmixing results could be obtained by our method.

Formation of a protein corona on silver nanoparticles mediates cellular toxicity via scavenger receptors.

Addition of a protein corona (PC) or protein adsorption layer on the surface of nanomaterials following their introduction into physiological environments may modify their activity, bio-distribution, cellular uptake, clearance, and toxicity. We hypothesize that silver nanoparticles (AgNPs) will associate with proteins common to human serum and cell culture media forming a PC that will impact cell activation and cytotoxicity. Furthermore, the role of scavenger receptor BI (SR-BI) in mediating this toxicity was evaluated. Citrate-suspended 20 nm AgNPs were incubated with human serum albumin (HSA), bovine serum albumin (BSA), high-density lipoprotein (HDL), or water (control) to form a PC. AgNPs associated with each protein (HSA, BSA, and HDL) forming PCs as assessed by electron microscopy, hyperspectral analysis, ζ-potential, and hydrodynamic size. Addition of the PC decreased uptake of AgNPs by rat lung epithelial and rat aortic endothelial cells. Hyperspectral analysis demonstrated a loss of the AgNP PC following internalization. Cells demonstrated concentration-dependent cytotoxicity following exposure to AgNPs with or without PCs (0, 6.25, 12.5, 25 or 50 μg/ml). All PC-coated AgNPs were found to activate cells by inducing IL-6 mRNA expression. A small molecule SR-BI inhibitor was utilized to determine the role of SR-BI in the observed effects. Pretreatment with the SR-BI inhibitor decreased internalization of AgNPs with or without PCs, and reduced both cytotoxicity and IL-6 mRNA expression. This study characterizes the formation of a PC on AgNPs and demonstrates its influence on cytotoxicity and cell activation through a cell surface receptor.

Characterization of chemical–physical, structural and morphological properties of biochars from biowastes produced at different temperatures

Biochar production from biowastes (e.g. digestate) is currently one of the more innovative and unexplored fields of research. A complete characterization of these materials, also according to the production temperature, would be a key tool to assess their potential use as soil amendments. For this purpose, five feedstocks (sewage sludge, municipal organic waste, cattle manure and silage digestates, poultry litter and vineyard pruning residues) were pyrolyzed at different temperatures. Structural and morphological transformations of biomasses during heating were followed by using FT-IR, scanning electron microscopy (SEM) and hyperspectral enhanced dark-field microscopy, a novel technique that provides both spectral and spatial information in one measurement. In addition, biochar microstructure (i.e. surface area and pore size distribution) using CO2 and N2 adsorption isotherms was investigated. Specific density was also analysed by a helium pycnometer. Biochars exhibited considerable chemical, structural and morphological differences depending on temperature and feedstock type. Moreover, specific density and surface area increased with the temperature. In particular, heating was able to produce a sharp increase of mesopore and micropore volume especially at 450 and 550 °C, but with different intensities for each feedstock. Thanks to the hyperspectral analysis, distinctive spectral patterns depending on the biochar chemical composition as well as the spatial distribution of the components were found. The results demonstrated that, from a physical–chemical point of view, it is not possible to identify an “ideal” biochar able to improve both soil nutrient content and structure. On the contrary, depending on feedstocks and temperature, each biochar exhibits specific features that would make it suitable for a specific purpose.

Determining the Effects of Storage on Cotton and Soybean Leaf Samples for Hyperspectral Analysis

This paper studies the effect of storage techniques for transporting collected plant leaves from the field to the laboratory for hyperspectral analysis. The strategy of collecting leaf samples in the field for laboratory analysis is typically used when ground truthing is needed in remote sensing studies. Results indicate that the accuracy of hyperspectral measurements depends on a combination of storage technique (in a cooler or outside a cooler), time elapsed between collecting leaf samples in the field and measuring in the laboratory, and the plant species. A nonlinear model fitting method is proposed to estimate the spectrum of decaying plant leaves. This revealed that the reflectance of soybean leaves remained within the normal range for 45 min when the leaves were stored in a cooler, while soybean leaves stored outside a cooler remained within the normal range for 30 min. However, cotton leaves stored in a cooler decayed faster initially. Regardless of storage technique, results indicate that up to a maximum of 30 min can elapse between plant leaf sampling in the field and hyperspectral measurements in the laboratory. This study focused on cotton and soybean leaves, but the implication that time elapsing between sampling leaves and measuring their spectrum should be limited as much as possible can be applied to any study on other crop leaves. Results of the study also provide a guideline for crop storage limits when analyzing by laboratory hyperspectral sensing setting to improve the quality and reliability of data for precision agriculture.

Estimating Canopy Nitrogen Content in a Heterogeneous Grassland with Varying Fire and Grazing Treatments: Konza Prairie, Kansas, USA

Quantitative, spatially explicit estimates of canopy nitrogen are essential for understanding the structure and function of natural and managed ecosystems. Methods for extracting nitrogen estimates via hyperspectral remote sensing have been an active area of research. Much of this research has been conducted either in the laboratory, or in relatively uniform canopies such as crops. Efforts to assess the feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures have been less extensive. In this study, we use in situ and aircraft hyperspectral data to assess several empirical methods for extracting canopy nitrogen from a tallgrass prairie with varying fire and grazing treatments. The remote sensing data were collected four times between May and September in 2011, and were then coupled with the field-measured leaf nitrogen levels for empirical modeling of canopy nitrogen content based on first derivatives, continuum-removed reflectance and ratio-based indices in the 562–600 nm range. Results indicated that the best-performing model type varied between in situ and aircraft data in different months. However, models from the pooled samples over the growing season with acceptable accuracy suggested that these methods are robust with respect to canopy heterogeneity across spatial and temporal scales.