Showing papers on "VNIR published in 2015"

Hyper-Sharpening: A First Approach on SIM-GA Data

Abstract: This paper aims at defining a new paradigm (hypersharpening) in remote sensing image fusion. In fact, due to the development of new instruments, thinking only in terms of pansharpening is reductive. Even though some expressions as hyperspectral (HS) pansharpening already exist, there is not a suitable definition when multispectral/hyperspectral data are used as source to extract spatial details. After defining the hypersharpening framework, we draw the readers’ attention to its peculiar characteristics, by proposing and evaluating two hypersharpening methods. Experiments are carried out on the data produced by the updated version of SIM-GA imager, designed by Selex ES, which is composed by a panchromatic camera and two spectrometers in the VNIR and SWIR spectral ranges, respectively. Owing to the different resolution factors among panchromatic, VNIR and SWIR data sets, we can apply hypersharpening to fuse SWIR data to VNIR resolution. Comparisons of hypersharpening with “traditional” pansharpening show hypersharpening is more effective.

Estimating a Soil Quality Index with VNIR Reflectance Spectroscopy

Abstract: Sensor-based approaches to assessment and quantification of soil quality are important to facilitate cost-effective, site-specific soil management. The objective of this research was to evaluate the ability of visible, near-infrared (VNIR) diffuse reflectance spectroscopy to estimate multiple soil quality indicators (SQIs) and Soil Management Assessment Framework (SMAF) scores. A total of 234 soil samples from two depths (0–5 and 5–15 cm) were obtained in 2008 from 17 agricultural management systems located in the Central Claypan Region of Missouri, USA. The VNIR spectra were obtained on oven-dried and field–moist soil, and calibration models were developed with partial least squares (PLS) regression. Models were evaluated using the coefficient of determination (R²), residual prediction deviation (RPD), and the ratio of prediction error to interquartile range (RPIQ). The most reliable estimation results were achieved using oven-dry soil for organic C, β-glucosidase, total N, the biological SMAF score, the organic C score, and the β-glucosidase score (R² ≥ 0.76, RPD ≥ 2.0, RPIQ ≥ 3.2). Using field-moist soils, the most reliable estimation results were achieved for organic C and the organic C score (R² ≥ 0.80, RPD ≥ 2.1, RPIQ ≥ 3.6). Incorporating the bulk density score and P score as auxiliary variables with the VNIR spectra improved estimation of the overall SMAF soil quality score for oven-dry soil (R² = 0.76, RPD = 2.0, RPIQ = 3.1) and field-moist soil (R² = 0.75, RPD = 1.9, RPIQ = 2.8). These results demonstrate the robustness of VNIR estimation of biological SQIs, and illustrate the potential for rapid, in-field quantification of soil quality by fusing VNIR sensors with auxiliary data obtained from complementary sensors or supplemental analyses.

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

Abstract: The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R2) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² superior to 0.76 and Root mean square errors inferior to 8.3% m3∙m−3 in all cases) and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

VNIR spectral characteristics of terrestrial igneous effusive rocks: mineralogical composition and the influence of texture

Abstract: Abstract Visible and Near-Infrared (VNIR) reflectance spectroscopy is an important technique with which to map mineralogy and mineralogical variations across planetary surfaces using remotely sensed data. Absorption bands in this spectral range are due to electronic or molecular processes directly related to mineral families or specific compositions. Effusive igneous rocks are widely recognized materials distributed on the surfaces of terrestrial planets, and are formed by primary minerals that can be discriminated by electronic absorptions (e.g. crystal field absorption). In this paper, we review the current knowledge of effusive rock compositions obtained by crystal field absorption in VNIR reflectance spectroscopy, and consider how different petrographical characteristics influence the mineralogical interpretation of such rock compositions. We show that: (1) the dominant mineralogy can be clearly recognized for crystalline material, especially with relatively large crystal dimension groundmass or high porphyritic index; (2) both grain and crystal size are important factors that influence the spectra of effusive rocks where groundmass is generally characterized by microscopic crystals; and (3) glassy dark components in the groundmass reduce or hide the crystal field absorption of mafic minerals or plagioclase otherwise expected to be present.

Using VNIR and SWIR field imaging spectroscopy for drought stress monitoring of beech seedlings

Abstract: Drought stress is expected to become a recurrent problem for central European forests due to regional climate change. In order to study the effects on one of the most common tree species in Germany, the European beech Fagus sylvatica, young potted beech trees were exposed to drought stress in a controlled experiment and their reaction was observed using visible/near-infrared VNIR and shortwave infrared SWIR field imaging spectroscopy cameras mounted on a platform. Equivalent water thickness EWT and leaf chlorophyll content LCC were measured and partial least squares PLS regression models were trained using these reference measurements and reflectance spectra of the trees. The models were applied to create maps of these properties with a spatial resolution in the millimetre range. These maps can be used to study the spatial distribution of EWT and LCC for single leaves or even for intra-leaf variability. Both properties can be estimated using only the VNIR sensor, but EWT estimation improves considerably by also incorporating SWIR data. LCC estimations with SWIR data alone do not work satisfactorily.

Near-infrared spectroscopy of lacustrine sediments in the Great Salt Lake Desert: An analog study for Martian paleolake basins

Abstract: The identification and characterization of aqueous minerals within ancient lacustrine environments on Mars are a high priority for determining the past habitability of the red planet. Terrestrial analog studies are useful both for understanding the mineralogy of lacustrine sediments, how the mineralogy varies with location in a lacustrine environment, and for validating the use of certain techniques such as visible–near-infrared (VNIR) spectroscopy. In this study, sediments from the Pilot Valley paleolake basin of the Great Salt Lake desert were characterized using VNIR as an analog for Martian paleolake basins. The spectra and subsequent interpretations were then compared to mineralogical characterization by ground truth methods, including X-ray diffraction, automated scanning electron microscopy, and several geochemical analysis techniques. In general, there is good agreement between VNIR and ground truth methods on the major classes of minerals present in the lake sediments and VNIR spectra can also easily discriminate between clay-dominated and salt-dominated lacustrine terrains within the paleolake basin. However, detection of more detailed mineralogy is difficult with VNIR spectra alone as some minerals can dominate the spectra even at very low abundances. At this site, the VNIR spectra are dominated by absorption bands that are most consistent with gypsum and smectites, though the ground truth methods reveal more diverse mineral assemblages that include a variety of sulfates, primary and secondary phyllosilicates, carbonates, and chlorides. This study provides insight into the limitations regarding the use of VNIR in characterizing complex mineral assemblages inherent in lacustrine settings.

Design and Fabrication of a Real-Time Measurement System for the Capsaicinoid Content of Korean Red Pepper (Capsicum annuum L.) Powder by Visible and Near-Infrared Spectroscopy

Abstract: This research aims to design and fabricate a system to measure the capsaicinoid content of red pepper powder in a non-destructive and rapid method using visible and near infrared spectroscopy (VNIR). The developed system scans a well-leveled powder surface continuously to minimize the influence of the placenta distribution, thus acquiring stable and representative reflectance spectra. The system incorporates flat belts driven by a sample input hopper and stepping motor, a powder surface leveler, charge-coupled device (CCD) image sensor-embedded VNIR spectrometer, fiber optic probe, and tungsten halogen lamp, and an automated reference measuring unit with a reference panel to measure the standard spectrum. The operation program includes device interface, standard reflectivity measurement, and a graphical user interface to measure the capsaicinoid content. A partial least square regression (PLSR) model was developed to predict the capsaicinoid content; 44 red pepper powder samples whose measured capsaicinoid content ranged 13.45–159.48 mg/100 g by per high-performance liquid chromatography (HPLC) and 1242 VNIR absorbance spectra acquired by the pungency measurement system were used. The determination coefficient of validation (RV2) and standard error of prediction (SEP) for the model with the first-order derivative pretreatment method for Korean red pepper powder were 0.8484 and ±13.6388 mg/100 g, respectively.

Optical constants of synthetic potassium, sodium, and hydronium jarosite

Abstract: The hydroxy sulfate jarosite [(K,Na,H 3 O)Fe 3 (SO 4 ) 2 (OH) 6 ] has both been discovered on Mars, and is associated with areas of highly acidic runoff on Earth. Because jarosite is extremely sensitive to formation conditions, it is an important target mineral for remote sensing applications. Yet at visible and near infrared (VNIR) wavelengths, where many spacecraft spectrometers collect data, the spectral abundance of a mineral in a mixture is not linearly correlated with the surface abundance of that mineral. Radiative transfer modeling can be used to extract quantitative abundance estimates if the optical constants (the real and imaginary indices of refraction, n and k ) for all minerals in the mixture are known. Unfortunately, optical constants for a wide variety of minerals, including sulfates like jarosite, are not available. This is due, in part, to the inherent difficulty in obtaining such data for minerals that tend to crystallize naturally as fine-grained (~10 μm) powders, like many sulfates including jarosite. However, the optical constants of powders can be obtained by inverting the equation of radiative transfer and using it to model laboratory spectra. In this paper, we provide robust n and k data for synthetic potassium, hydronium, and sodium jarosite in the VNIR. We also explicitly describe the calculation procedures (including access to our Matlab code) so that others may obtain optical constants of additional minerals. Expansion of the optical constants library in the VNIR will facilitate the extraction of quantitative mineral abundances, leading to more in-depth evaluations of remote sensing target locations.

Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging

Abstract: Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400-1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557-701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce.

Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on "The Improvement of Land Cover Classification

Abstract: Much remote sensing (RS) research focuses on fusing, i.e., combining, multi-resolution/multi-sensor imagery for land use/land cover (LULC) classification. In relation to this topic, Sun and Schulz (1) recently found that a combination of visible-to-near infrared (VNIR; 30 m spatial resolution) and thermal infrared (TIR; 100-120 m spatial resolution) Landsat data led to more accurate LULC classification. They also found that using multi-temporal TIR data alone for classification resulted in comparable (and in some cases higher) classification accuracies to the use of multi-temporal VNIR data, which contrasts with the findings of other recent research (2). This discrepancy, and the generally very high LULC accuracies achieved by Sun and Schulz (up to 99.2% overall accuracy for a combined VNIR/TIR classification result), can likely be explained by their use of an accuracy assessment procedure which does not take into account the multi-resolution nature of the data. Sun and Schulz used 10-fold cross-validation for accuracy assessment, which is not necessarily inappropriate for RS accuracy assessment in general. However, here it is shown that the typical pixel-based cross-validation approach results in non-independent training and validation data sets when the lower spatial resolution TIR images are used for classification, which causes classification accuracy to be overestimated.

Using the Surface Temperature-Albedo Space to Separate Regional Soil and Vegetation Temperatures from ASTER Data

Abstract: Soil and vegetation component temperatures in non-isothermal pixels encapsulate more physical meaning and are more applicable than composite temperatures. The component temperatures however are difficult to be obtained from thermal infrared (TIR) remote sensing data provided by single view angle observations. Here, we present a land surface temperature and albedo (T-α) space approach combined with the mono-surface energy balance (SEB-1S) model to derive soil and vegetation component temperatures. The T-α space can be established from visible and near infrared (VNIR) and TIR data provided by single view angle observations. This approach separates the soil and vegetation component temperatures from the remotely sensed composite temperatures by incorporating soil wetness iso-lines for defining equivalent soil temperatures; this allows vegetation temperatures to be extracted from the T-α space. This temperature separation methodology was applied to advanced scanning thermal emission and reflection radiometer (ASTER) VNIR and high spatial resolution TIR image data in an artificial oasis area during the entire growing season. Comparisons with ground measurements showed that the T-α space approach produced reliable soil and vegetation component temperatures in the study area. Low root mean square error (RMSE) values of 0.83 K for soil temperatures and 1.64 K for vegetation temperatures, respectively, were obtained, compared to component temperatures measurements from a ground-based thermal camera. These results support the use of soil wetness iso-lines to derive soil surface temperatures. It was also found that the estimated vegetation temperatures were extremely close to the near surface air temperature observations when the landscape is well watered under full vegetation cover. More robust soil and vegetation temperature estimates will improve estimates of soil evaporation and vegetation transpiration, leading to more reliable the monitoring of crop water stress and drought.

Scale issues related to the accuracy assessment of land use/land cover maps produced using multi-resolution data

Abstract: Much remote sensing (RS) research focuses on fusing, i.e., combining, multi-resolution/multi-sensor imagery for land use/land cover (LULC) classification. In relation to this topic, Sun and Schulz [1] recently found that a combination of visible-to-near infrared (VNIR; 30 m spatial resolution) and thermal infrared (TIR; 100–120 m spatial resolution) Landsat data led to more accurate LULC classification. They also found that using multi-temporal TIR data alone for classification resulted in comparable (and in some cases higher) classification accuracies to the use of multi-temporal VNIR data, which contrasts with the findings of other recent research [2]. This discrepancy, and the generally very high LULC accuracies achieved by Sun and Schulz (up to 99.2% overall accuracy for a combined VNIR/TIR classification result), can likely be explained by their use of an accuracy assessment procedure which does not take into account the multi-resolution nature of the data. Sun and Schulz used 10-fold cross-validation for accuracy assessment, which is not necessarily inappropriate for RS accuracy assessment in general. However, here it is shown that the typical pixel-based cross-validation approach results in non-independent training and validation data sets when the lower spatial resolution TIR images are used for classification, which causes classification accuracy to be overestimated.

Measuring Stress Reactions of Beech Seedlings with PRI, Fluorescence, Temperatures and Emissivity from VNIR and Thermal Field Imaging Spectroscopy

Abstract: Photosynthesis rate was measured during the course of a day on a pot of well-watered and a pot of drought-stressed young beech trees At the same time, hyperspectral visible/near infrared and hyper

Impact of Improved Calibration of a NEO HySpex VNIR-1600 Sensor on Remote Sensing of Water Depth

Abstract: This paper investigates at the example of bathymetry how much an application can profit from comprehensive characterizations required for an improved calibration of data from a state-of-the-art commercial hyperspectral sensor. A NEO HySpex VNIR-1600 sensor is used for this paper, and the improvements are based on measurements of sensor properties not covered by the manufacturer, in particular, detector nonlinearity and stray light. This additional knowledge about the instrument is used to implement corrections for nonlinearity, stray light, spectral smile distortion and nonuniform spectral bandwidth and to base the radiometric calibration on a SI-traceable radiance standard. Bathymetry is retrieved from a data take from the lake Starnberg using WASI-2D. The results using the original and improved calibration procedures are compared with ground reference data, with an emphasis on the effect of stray-light correction. For our instrument, stray-light biases the detector response from 416–500 nm up to 8% and from 700–760 nm up to 5%. Stray-light-induced errors affect bathymetry mainly in water deeper than Secchi depth, whereas in shallower water, the dominant error source is the calibration accuracy of the light source used for radiometric calibration. Stray-light correction reduced the systematic error of water depth by 19% from Secchi depth to three times Secchi depth, whereas the relative standard deviation remained stable at 5%.

On the use of shortwave infrared for tree species discrimination in tropical semideciduous forest

Abstract: Tree species mapping in tropical forests provides valuable insights for forest managers. Keystone species can be located for collection of seeds for forest restoration, reducing fieldwork costs. However, mapping of tree species in tropical forests using remote sensing data is a challenge due to high floristic and spectral diversity. Little is known about the use of different spectral regions as most of studies performed so far used visible/near-infrared (390-1000 nm) features. In this paper we show the contribution of shortwave infrared (SWIR, 1045-2395 nm) for tree species discrimination in a tropical semideciduous forest. Using high-resolution hyperspectral data we also simulated WorldView-3 (WV-3) multispectral bands for classification purposes. Three machine learning methods were tested to discriminate species at the pixel-level: Linear Discriminant Analysis (LDA), Support Vector Machines with Linear (L-SVM) and Radial Basis Function (RBF-SVM) kernels, and Random Forest (RF). Experiments were performed using all and selected features from the VNIR individually and combined with SWIR. Feature selection was applied to evaluate the effects of dimensionality reduction and identify potential wavelengths that may optimize species discrimination. Using VNIR hyperspectral bands, RBF-SVM achieved the highest average accuracy (77.4%). Inclusion of the SWIR increased accuracy to 85% with LDA. The same pattern was also observed when WV-3 simulated channels were used to classify the species. The VNIR bands provided and accuracy of 64.2% for LDA, which was increased to 79.8 % using the new SWIR bands that are operationally available in this platform. Results show that incorporating SWIR bands increased significantly average accuracy for both the hyperspectral data and WorldView-3 simulated bands.

Integrated visible to near infrared, short wave infrared, and long wave infrared spectral analysis for surface composition mapping near Mountain Pass, California

Abstract: We have developed new methods for enhanced surface material identification and mapping that integrate visible to near infrared (VNIR, ~0.4 – 1 μm), short wave infrared (SWIR, ~1 – 2.5 μm), and long wave infrared (LWIR, ~8 – 12 μm) multispectral and hyperspectral imagery. This approach produces a single map of surface composition derived from the full spectral range. We applied these methods to a spectrally diverse region around Mountain Pass, CA. A comparison of the integrated results with those obtained from analyzing the spectral ranges individually reveals compositional information not exhibited by the VNIR, SWIR or LWIR data alone. We also evaluate the benefit of hyperspectral rather than multispectral LWIR data for this integrated approach.

VNIR Soil Spectroscopy for Field Soil Analysis

Abstract: The advent of affordable, ground-based, global positioning information (GPS)–enabled sensor technologies provides a new method to rapidly acquire georeferenced soil datasets in situ for high-resolution soil attribute mapping. Our research deployed vehicle-mounted electromagnetic sensor survey equipment to map and quantify soil variability (˜50 ha per day) using apparent electrical conductivity as an indirect measure of soil texture and moisture differences. A portable visible–near infrared (VNIR) spectrometer (350–2500 nm) was then used in the field to acquire hyperspectral data from the side of soil cores to a specified depth at optimized sampling locations. The sampling locations were derived by statistical analysis of the electromagnetic survey dataset, to proportionally sample the full range of spatial variability. The VNIR spectra were used to predict soil organic carbon (prediction model using field-moist spectra: R2 = 0.39; RPD = 1.28; and air-dry spectra: R2 = 0.80; RPD = 2.25). These point values...

Multi-modal optoelectronic vision system and uses thereof

Abstract: The disclosure relates to assemblies, kits and methods for multi-mode optoelectronic observation and sighting system with cross-platform integration capability. More particularly, the disclosure relates to assemblies, kits and methods facilitating the analog fusion of VNIR and LWIR image sensors' data to a single, coherent display.

Application of ASTER SWIR data on detection of alteration zone in the Sheikhabad area, eastern Iran

Abstract: This research was focused on different techniques of remote sensing to identify the external alteration structures at Sheikhabad region, especially mining exploration of hydrothermal alteration of typical structure using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. We have applied the ASTER data, including three visible and near infrared (VNIR), six shortwave infrared (SWIR), and five thermal infrared (TIR) bands to determine the alteration minerals of Sheikhabad area. Spectral analysis of the surface reflectance SWIR manifests absorption in 2.20 and 2.33 μm wavelength regions. Absorption at 2.20 μm (band 6), due to Al-OH anionic agent is consistent with the presence of clay minerals (illite, kaolinite) and sericite, whereas absorption at 2.33 μm (band 8) due to Mg-OH anionic agent and carbonates is consistent with the occurrence of chlorite, epidote, and calcite minerals. In this research, by using the satellite image processing of ASTER sensor, different methods such as false color composite (FCC), band ratio (BR), and principal component analysis (PCA) as well as the direct data from the Mokhtaran geological map (1:100,000) were applied. The results obtained from ASTER data are compatible with the conclusions inferred from petrology and X-ray diffraction (XRD) studies of the surface samples. Based on petrographic studies, these rocks have different compositions and include trachyandesite, andesite, rhyodacite, and porphyric diorite. According to petrology and chemical analysis (XRD) and remote sensing results, six alterations zones consisting of kaolinitization, silicification, alunitization, carbonatization, sericitization, and propylitic are distinguished; thus, it is suggested to explore Cu-Au mineralization.

Inter-Band Radiometric Comparison and Calibration of ASTER Visible and Near-Infrared Bands

Abstract: The present study evaluates inter-band radiometric consistency across the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near-infrared (VNIR) bands and develops an inter-band calibration algorithm to improve radiometric consistency. Inter-band radiometric comparison of current ASTER data shows a root mean square error (RMSE) of 3.8%–5.7% among radiance outputs of spectral bands due primarily to differences between calibration strategies of the NIR band for nadir-looking (Band 3N) and the other two bands (green and red bands, corresponding to Bands 1 and 2). An algorithm for radiometric calibration of Bands 2 and 3N with reference to Band 1 is developed based on the band translation technique and is used to obtain new radiometric calibration coefficients (RCCs) for sensor sensitivity degradation. The systematic errors between radiance outputs are decreased by applying the derived RCCs, which result in reducing the RMSE from 3.8%–5.7% to 2.2%–2.9%. The remaining errors are approximately equal to or smaller than the intrinsic uncertainties of inter-band calibration derived by sensitivity analysis. Improvement of the radiometric consistency would increase the accuracy of band algebra (e.g., vegetation indices) and its application. The algorithm can be used to evaluate inter-band radiometric consistency, as well as for the calibration of other sensors.

Remote sensing type urban image extracting method

Abstract: The invention provides a remote sensing type urban image extracting method. The method comprises the following steps: S1, performing characteristics extracting for altitude data of an ASTER VNIR satellite remote sensing image and derived products and a sample of a PALSAR HH/HV satellite remote sensing image; S2, extracting obvious urban and non-urban points based on the spectral characteristics of the urban and non-urban part; S3, performing confidence spreading by the LLGC using the obvious urban and non-urban points as the initial information based on the characteristics distribution feature of data to be classified, so as to obtain an urban confidence map; S4, obtaining the confidence of the whole remote sensing image, weighting and randomly sampling to obtain a training sample; S5, classifying the urban based on SVM, namely, classifying by the SVM method on the basis of the characteristic vectors extracted in the step S1 and the sample data extracted in step S4, and then obtaining a urban map subjected to binarization according to a classification label. With the adoption of the method, the problems of high cost and high time consumption and the like caused by manual sampling in the prior art can be solved.

Exploration of integrated visible to near-, shortwave-, and longwave-infrared (full range) hyperspectral data analysis

Abstract: Visible to near-, shortwave-, and longwave-infrared (VNIR, SWIR, LWIR) hyperspectral data were integrated using a variety of approaches to take advantage of complementary wavelength-specific spectral characteristics for improved material classification. The first approach applied separate minimum noise fraction (MNF) transforms to the three regions and combined only non-noise transformed bands. A second approach integrated the VNIR, SWIR, and LWIR data before using MNF analysis to isolate linear band combinations containing high signal to noise. Spectral endmembers extracted from each integrated dataset were unmixed and spatially mapped using a partial unmixing approach. Integrated results were compared to baseline analyses of the separate spectral regions. Outcomes show that analyzing across the full VNIR-SWIR-LWIR spectrum improves material characterization and identification.