Hyperspectral Signatures Research Articles

Night-Time Ground Hyperspectral Imaging for Urban-Scale Remote Sensing of Ambient PM—Modal Concentrations Retrieval

Retrieval of aerosol loading in vertical atmospheric columns is a common product of satellite and ground instruments that measure spectral extinction of solar radiation throughout the entire atmosphere. Here we study ground hyperspectral imaging of artificial light sources as a complementary method for retrieving fine aerosol concentrations along quazi-horizontal ambient open paths. Previously, we reported hyperspectral measurements of the aerosol optical thickness in the 500-900 nm range over urban-scale distances (180 m to 4 km), measuring the extinction of radiation emitted from a halogen source. Here we confirm in a laboratory-setup the basic premise that different accumulation-size aerosols generate distinct hyperspectral signatures in this spectral range. Measured hyperspectral attenuation signatures of fine aerosols were comparable to calculated Mie scattering signatures, suggesting that modal aerosol concentrations can be retrieved. A genetic algorithm was adapted to estimate the aerosol modal concentrations from its hyperspectral extinction signature. Retrievals of aerosol concentrations from measured and synthetic hyperspectral signatures indicated a robust algorithm, with an expected retrieval error of 0.2-22% for typical ambient concentrations along an urban-scale open path. The retrieval accuracy was found to depend on the relative aerosol modal concentrations, especially when there is a substantial overlap between the modal spectral signatures.

A Study on the Potential of Satellite Image-derived Hyperspectral Signatures to Assess the Grades of Iron ore Deposits

Abstract This paper present the results of a study made to assess the grades of iron ore deposits in parts of Noamundi area using the hyperspectral image data (EO-1 Hyperion). The study involves hyperspectral image data, preprocessing (removal of atmospheric effects), spectral curve generation from the image, extraction of certain spectral parameters and spectral data analysis. Atmospheric correction was carried out by using algorithms involving atmospheric analysis of spectral hyper-cubes and Quick Atmospheric Correction modules. Spectral curves generated for the pixels that contain iron ores resemble the spectral signature of iron ores, i.e., very strong absorption at 850-900nm region is observed. The strength of the absorption varies spatially at different pixels. Hence, spectral parameters such as radius of curvature of NIR absorption trough (750-1000nm), position of NIR absorption trough and distance from a reference(100% reflectance) line have been derived for all the image-derived spectra and were compared with the concentration of iron in the ore samples of mines. Empirical models were generated by relating the image-derived spectral parameters of iron ore locations and geochemistry of the samples collected from the same location which shows a reasonable match. Strong negative correlation is seen between the radius of curvature of the NIR absorption trough and concentration of iron with a R2 value of 0.809. The position of the NIR absorption trough is seen to shift towards longer wavelengths with decreasing iron content and the distance from the reference line increases with increasing iron content. Thus, this study indicates that it is feasible to discriminate the grades of iron ores from the spectral parameters derived from Hyperspectral satellite images.

A study on the hyperspectral signatures of sandy soils with varying texture and water content

This paper examines the hyperspectral signatures (in the visible near-infrared (VNIR)–shortwave infrared (SWIR) regions) of different samples of sandy soils possessing varying grain size and water content. Ten samples of sandy soils with differing textures and water contents were examined using a hyperspectral radiometer operating in the 350–2,500-nm range, and the spectral curves were obtained. Analyses of the curves indicate that grain size of sand has a considerable influence on the spectral response in the visible, NIR and SWIR regions, i.e. there is a decrease in the overall reflectance with increase in grain size. While the 350–575-nm region exhibits maximum overlap of spectral reflectance and, hence, least spectral separability between the soil types, the 2,000–2,100-nm region exhibits highest spectral separability between the sand samples of different textures. As regards the water content, it is seen that overall reflectance increases with decreasing water content in the sample, especially in the 1,400–1,800-nm regions. Further, the slope of the curves in the 1,890–2,100-nm region shows a well-defined relationship with the water content. Based on these well-defined relations, it is inferred that hyperspectral radiometry in the VNIR and SWIR regions can be used to estimate the texture and water content of sandy soils.

Locality Preserving Genetic Algorithms for Spatial-Spectral Hyperspectral Image Classification

Recent developments in remote sensing technologies have made hyperspectral imagery (HSI) readily available to detect and classify objects on the earth using pattern recognition techniques. Hyperspectral signatures are composed of densely sampled reflectance values over a wide range of the spectrum. Although most of the traditional approaches for HSI analysis entail per-pixel spectral classification, spatial-spectral exploitation of HSI has the potential to further improve the classification performance-particularly when there is unique class-specific textural information in the scene. Since the dimensionality of such remotely sensed imagery is often very large, especially in spatial-spectral feature domain, a large amount of training data is required to accurately model the classifier. In this paper, we propose a robust dimensionality reduction approach that effectively addresses this problem for hyperspectral imagery (HSI) analysis using spectral and spatial features. In particular, we propose a new dimensionality reduction algorithm, GA-LFDA where a Genetic Algorithm (GA) based feature selection and Local-Fisher's Discriminant Analysis (LFDA) based feature projection are performed in a raw spectral-spatial feature space for effective dimensionality reduction. This is followed by a parametric Gaussian mixture model classifier. Classification results with experimental data show that our proposed method outperforms traditional dimensionality reduction and classification algorithms in challenging small training sample size and mixed pixel conditions.

Estimating physiological skin parameters from hyperspectral signatures

We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers.

An adaptive similarity measure for classification of hyperspectral signatures

Capturing both the shape of the spectral continuum and the positions/widths of absorption bands is essential to accurately measure similarity between hyperspectral signatures. Furthermore, the relative importance of these features is data dependent. In this letter, we present an adaptive version of our recently proposed continuum-intact (CI)/continuum-removed (CR) similarity measure which automatically determines a convex weighting between similarity measurements of CI and CR signatures according to input data. We describe an efficient technique to calculate an optimal weight for a linear combination of CI and CR similarity measurements. We evaluate the technique on the Airborne Visible/Infrared Imaging Spectrometer spectra sampled from a well-studied urban scene and show that our technique yields improved classification accuracy in comparison to CI or CR similarity measurements alone and performs comparably to calculating the weight via brute-force search, at a much reduced computational cost. A source code implementation of our algorithm is provided online.

Development of spectral indices for detecting and identifying plant diseases

Spectral vegetation indices (SVIs) have been shown to be useful for an indirect detection of plant diseases. However, these indices have not been evaluated to detect or to differentiate between plant diseases on crop plants. The aim of this study was to develop specific spectral disease indices (SDIs) for the detection of diseases in crops. Sugar beet plants and the three leaf diseases Cercospora leaf spot, sugar beet rust and powdery mildew were used as model system. Hyperspectral signatures of healthy and diseased sugar beet leaves were assessed with a non-imaging spectroradiometer at different developing stages and disease severities of pathogens. Significant and most relevant wavelengths and two band normalized differences from 450 to 950nm, describing the impact of a disease on sugar beet leaves were extracted from the data-set using the RELIEF-F algorithm. To develop hyperspectral indices for the detection of sugar beet diseases the best weighted combination of a single wavelength and a normalized wavelength difference was exhaustively searched testing all possible combinations. The optimized disease indices were tested for their ability to detect and to classify healthy and diseased sugar beet leaves. With a high accuracy and sensitivity healthy sugar beet leaves and leaves, infected with Cercospora leaf spot, sugar beet rust and powdery mildew were classified (balanced classification accuracy: 89%, 92%, 87%, 85%, respectively). Spectral disease indices were also successfully applied on hyperspectral imaging data and on non-imaging data from a sugar beet field. Specific disease indices will improve disease detection, identification and monitoring in precision agriculture applications.

Remote Sensing of Solar-Induced Chlorophyll Fluorescence at Atmospheric Oxygen Absorption Band Around 760 nm and Simulation of That Absorption in Laboratory

The present work establishes that the usual method of remote measurement of vegetation reflectance can also detect steady-state solar-induced fluorescence of vegetation. It identifies the hyperspectral signature of chlorophyll fluorescence as a small spike on the reflectance spectral curve of vegetation at the oxygen absorption (O2-A) band around 760 nm. It justifies the origin of the fluorescence signature with both leaf- and pigment-level studies and quantifies it with the following: (1) presence/absence of chlorophyll in leaves; (2) varying concentration of chlorophyll in solution; (3) time of exposure under illumination; and (4) correctness of the calibration of leaf reflectance with respect to the standard white surface. The O2-A band is detectable in solar radiation measured on Earth because of light absorption by the atmospheric oxygen column. This paper suggests an experimental technique to simulate that absorption in laboratory also. Oxygen gas compressed at high pressure can produce the oxygen absorption band artificially at indoor condition.

SPECTRAL CHARACTERISTICS OF SELECTED HERMATYPIC CORALS FROM GULF OF KACHCHH, INDIA

Abstract. Hermatypic, scleractinian corals are the most important benthic substrates in a coral reef ecosystem. The existing, high (spatial) resolution, broad-band, multi-spectral, space-borne sensors have limited capability to spatially detect and spectrally discriminate coral substrates. In situ hyperspectral signatures of eight coral targets were collected with the help of Analytical Spectral Devices FieldSpec spectroradiometer from Paga and Laku Point reefs of Gulf of Kachchh, India to study the spectral behaviour of corals. The eight coral targets consisted of seven live corals representing four distinct colony morphologies and one bleached coral target. The coral spectra were studied over a continuous range of 350 to 1350 nm. The corals strongly reflected in the NIR and MIR regions with regional central maximas located at 820 and 1070 nm respectively. In the visible region the live coral spectra conformed to "brown mode" of coral reflectance with triple-peaked pattern at 575, 600 and 650 nm. All coral spectra are characterized with two distinct absorption features: chlorophyll absorption at 675 nm and water absorption at 975 nm. The live and the bleached corals get distinguished in the visible region over 400 to 600 nm region. Water column over the targets modifies the spectral shape and magnitude. First and second-order derivatives help in identifying spectral windows to distinguish live and bleached corals.

RLQ and fourth-corner analysis of plant species traits and spectral indices derived from HyMap and CHRIS-PROBA imagery

Databases on plant traits as well as the availability of global coverage of high spatial and spectral resolution remote-sensing data are constantly growing. However, little effort has been made to analyse the relationship between plant traits and remote-sensing data while simultaneously taking species identity and abundance into consideration. We correlated quantitative and qualitative plant traits from a dwarf shrub savanna in Namibia, with spectral indices derived from two hyperspectral sensors, HyMap and the Compact High Resolution Imaging Spectrometer Project for On-Board Autonomy (CHRIS-PROBA), which differ in their spatial and spectral resolution. We used RLQ analysis and the fourth-corner statistic, which are two three-table ordination approaches that circumvent the so-called fourth-corner problem. A higher spatial resolution helped to identify trait–index correlations linked to vegetation structure, while a lower spatial resolution pointed at traits linked to vegetation cover. A higher spectral resolution did not improve the relationships between spectral indices and plant traits. However, continuous hyperspectral signatures allowed for the calculation of spectral indices that make use of the detailed spectra allowing for more sophisticated spectral indices. We propose RLQ and the fourth-corner statistic as suitable tools for the remote sensing and Earth observation community that allow the direct correlation of trait databases with remotely sensed information.

Hyperspectral leaf signature as an added dimension for species discrimination: case study of four tropical mangroves

Hyperspectral leaf reflectance of a plant provides unique information that is characteristic of that plant. The present investigation is a preliminary attempt to assess whether spectra of leaves of mangrove species recorded under field conditions contain adequate spectral information for discerning mangroves at species rank. The paper highlights the hyperspectral characteristics of leaf surfaces of four prominent tropical mangrove species, viz., Avicennia alba, Avicennia marina, Rhizophora mucronata and Sonneratia caseolaris, found in the tidal forests of India. Hyperspectral observations were recorded using a field spectroradiometer, and pre-processed and averaged reflectance values of samples for three types of arrangements, viz., (1) randomly arranged leaves, (2) dorsal leaf surfaces and (3) ventral leaf surfaces of the species were statistically tested using one-way ANOVA to see whether the values significantly differed at every spectral location. All the four species were statistically different at all the spectral locations with majority of the bands exhibiting 99% confidence level. Finally, discriminant analysis was performed to identify the bands for maximum separability for the three types of arrangement of the leaves of the species taken separately and in different combinations. The optimal Wilks’ Lambda (L) were achieved with: six, three, eleven, six, five, thirteen and eleven wavelengths for discriminating random leaves of the four species, dorsal and ventral surfaces of A. alba, A. marina, R. mucronata, S. caseolaris, upper leaf surfaces of all the species, lower leaf surfaces, respectively. Factor analysis was used as an added tool to identify the wavelengths that were uncorrelated and contained maximum information in the combination of selected wavelengths. The results confirmed the unique spectral signatures of the four species, which could be explained in terms of leaf properties unique to the species. Cellular structure and pigmentation of the isolateral leaves of S. caseolaris are very different from the dorsiventral ones of the other three, which significantly changed the reflectance characteristics of the species.

Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops.

Synthesis of mixed pixel hyperspectral signatures

The general method of analysing mixed pixel spectral response is to decompose the actual spectra into several pure spectral components representing the signatures of the endmembers. This work suggests a reverse engineering of standardizing the mixed pixel spectrum for a certain spatial distribution of endmembers by synthesizing spectral signatures with varying proportions of standard spectral library data and matching them with the experimentally obtained mixed pixel signature. The idea is demonstrated with hyperspectral ultraviolet–visible–near-infrared (UV–vis–NIR) reflectance measurements on laboratory-generated model mixed pixels consisting of different endmember surfaces: concrete, soil, brick and vegetation and hyperspectral signatures derived from Hyperion satellite images consisting of concrete, soil and vegetation in different proportions. The experimental reflectance values were compared with the computationally generated spectral variations assuming linear mixing of pure spectral signatures. Good matching in the nature of spectral variation was obtained in most cases. It is hoped that using the present concept, hyperspectral signatures of mixed pixels can be synthesized from the available spectral libraries and matched with those obtained from satellite images, even with fewer bands. Thus enhancing the computational job in the laboratory can moderate the keen requirement of high accuracy of remote-sensor and band resolution, thereby reducing data volume and transmission bandwidth.

Monitoring of Topsoil Characteristics Using Field-airborne Hyperspectral Signatures and Underground Soil Images from Real-time Soil Spectrophotometer (RTSS)

Monitoring of Topsoil Characteristics Using Field-airborne Hyperspectral Signatures and Underground Soil Images from Real-time Soil Spectrophotometer (RTSS)

Recursive spectral similarity measure-based band selection for anomaly detection inhyperspectral imagery

Band selection has been widely used in hyperspectral image processing for dimensionreduction. In this paper, a recursive spectral similarity measure-based band selection(RSSM-BBS) method is presented. Unlike most of the existing image-based band selectiontechniques, it is for two hyperspectral signatures with its main focus on their spectralseparability. Furthermore, it is unsupervised and based on the recursive calculation of thespectral similarity measure with an additional band. In order to demonstrate the utility ofthe proposed method, an anomaly detection algorithm is developed, which firstextracts the anomalous target spectrum from the image using the automatictarget detection and classification algorithm (ATDCA), followed by the maximumspectral screening (MSS) to obtain a good estimate of the background, and thenimplements RSSM-BBS to select bands that participate in the subsequent adaptivecosine/coherence estimator (ACE) target detection. As shown in the experimentalresult on the AVIRIS dataset, the detection performance of the ACE has beenimproved greatly with the bands selected by RSSM-BBS over that using full bands.

Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

Summary1. This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water‐quality standards.2. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near‐infrared (NIR)‐Green Angle Index (NGAI) and normalized water absorption depth (DH), at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations.3. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral‐derived NDVI. The IKONOS‐based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the DH.4. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High‐resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages.5. Interpretation of biophysical parameters derived from high‐resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments.

A logistic radial basis function regression method for discrimination of cover crops in olive orchards

Olive ( Olea europaea L.) is the main perennial Spanish crop. Soil management in olive orchards is mainly based on intensive and tillage operations, which have a great relevancy in terms of negative environmental impacts. Due to this reason, the European Union (EU) only subsidizes cropping systems which require the implementation of conservation agro-environmental techniques such as cover crops between the rows. Remotely sensed data could offer the possibility of a precise follow-up of presence of cover crops to control these agrarian policy actions, but firstly, it is crucial to explore the potential for classifying variations in spectral signatures of olive trees, bare soil and cover crops using field spectroscopy. In this paper, we used hyperspectral signatures of bare soil, olive trees, and sown and dead cover crops taken in spring and summer in two locations to evaluate the potential of two methods (MultiLogistic regression with Initial and Radial Basis Function covariates, MLIRBF; and SimpleLogistic regression with Initial and Radial Basis Function covariates, SLIRBF) for classifying them in the 400–900 nm spectrum. These methods are based on a MultiLogistic regression model formed by a combination of linear and radial basis function neural network models. The estimation of the coefficients of the model is carried out basically in two phases. First, the number of radial basis functions and the radii and centres’ vector are determined by means of an evolutionary neural network algorithm. A maximum likelihood optimization method determines the rest of the coefficients of a MultiLogistic regression with a set of covariates that include the initial variables and the radial basis functions previously estimated. Finally, we apply forward stepwise techniques of structural simplification. We compare the performance of these methods with robust classification methods: Logistic Regression without covariate selection, MLogistic; Logistic Regression with covariate selection, SLogistic; Logistic Model Trees algorithm (LMT); the C4.5 induction tree; Naïve Bayesian tree algorithm (NBTree); and boosted C4.5 trees using AdaBoost.M1 with 10 and 100 boosting iterations. MLIRBF and SLIRBF models were the best discriminant functions in classifying sown or dead cover crops from olive trees and bare soil in both locations and seasons by using a seven-dimensional vector with green (575 nm), red (600, 625, 650 and 675 nm), and near-infrared (700 and 725 nm) wavelengths as input variables. These models showed a correct classification rate between 95.56% and 100% in both locations and seasons. These results suggest that mapping covers crops in olive trees could be feasible by the analysis of high resolution airborne imagery acquired in spring or summer for monitoring the presence or absence of cover crops by the EU or local administrations in order to make the decision on conceding or not the subsidy.

Performance of Kriging-Based Soft Classification on WiFS/IRS-1D Image Using Ground Hyperspectral Signatures

Hard/soft classification techniques are the conventional ways of image classification on satellite data. These classifiers have a number of drawbacks. First, these approaches are inappropriate for mixed pixels. Second, these approaches do not consider spatial variability. Kriging-based soft classification (KBSC) is a nonparametric geostatistical method. It exploits the spatial variability of the classes within the image. This letter compares the performance of KBSC with other conventional hard/soft classification techniques. The satellite data used in this study is the Wide Field Sensor from the Indian Remote Sensing Satellite-1D (IRS-1D). The ground hyperspectral signatures acquired from the agricultural fields by a handheld spectroradiometer are used to detect subpixel targets from the satellite images. Two measures of closeness have been used for the accuracy assessment of the KBSC to that of the conventional classifications. The results prove that the KBSC is statistically more accurate than the other conventional techniques.

Improving multispectral mapping by spectral modeling with hyperspectral signatures

Hyperspectral imaging (HSI) data in the 0.4 - 2.5 micrometer spectral range allow direct identification of materials using their spectral signatures, however, spatial coverage is limited. Multispectral Imaging (MSI) data are spectrally undersampled and may not allow unique identification, but they do provide synoptic spatial coverage. We have developed an approach that uses coincident HSI/MSI data to extend mineral mapping to larger areas. Hyperspectral data are used to model and extend signatures to multispectral Advanced Spaceborne Thermal Emmission and Reflection Radiometer (ASTER) data. Analysis consists of 1. Atmospheric correction of both the hyperspectral and multispectral data, 2. Analysis of the hyperspectral data to determine spectral endmembers and their spatial distributions, 3. Spectral modeling to convert the hyperspectral signatures to the multispectral response, and 4. Analysis of the MSI data to extend mapping to the larger spatial coverage of the multispectral data. Comparing overlapping area with extensive field verification shows that ASTER mineral mapping using these methods approaches 70% accuracy compared to HSI for selected minerals. Spot checking of extended ASTER mapping results also shows good correspondence. While examples shown are specific to ASTER Short Wave Infrared (SWIR) data, the approach could also be used for other multispectral sensors and spectral ranges.

Effects of Resonances in Corrugated Horn Antennas for a 22-GHz Balancing Radiometer

Hard and soft classification techniques are the conventional ways of image classification on satellite data. These classifiers have number of drawbacks. Firstly, these approaches are inappropriate for mixed pixels. Secondly, these approaches do not consider spatial variability. Kriging based soft classifier (KBSC) is a non-parametric geostatistical method. It exploits the spatial variability of the classes within the image. This letter compares the performance of KBSC with other conventional hard/soft classification techniques. The satellite data used in this study is the Wide Field Sensor (WiFS) from the Indian Remote Sensing Satellite -1D (IRS-1D). The ground hyperspectral signatures acquired from the agricultural fields by a hand held spectroradiometer are used to detect subpixel targets from the satellite images. Two measures of closeness have been used for accuracy assessment of the KBSC to that of the conventional classifications. The results prove that the KBSC is statistically more accurate than the other conventional techniques.