Hyperspectral Imaging Sensors Research Articles

Remote sensing and geochemistry for detecting hydrocarbon microseepages

This work tests the hypothesis that chemical and mineralogical alterations in rocks and soils are related to hydrocarbon microseepages above some of the major oil fields. In this study we used Hyperion hyperspectral imaging sensors to map alterations that appear to be associated with hydrocarbon microseepages in the Patrick Draw area of Wyoming. Our mineralogical, geochemical, and carbon isotope data support the presence of hydrocarbon microseepages. Satellite imagery training classifications defined by areas of hydrocarbon microseepage have resulted in the successful identification of the areal extent of an anomalous area. Geochemical characteristics of samples that define this anomalous area were then compared to the remaining non-anomalous samples using X-ray diffraction (XRD), reflectance spectroscopy, and carbon isotope techniques. XRD analyses demonstrated the increased presence of feldspars in non-anomalous samples compared to anomalous samples. Spectroscopy results demonstrated higher proportions of clays within the anomalous samples compared to non-anomalous samples. The δ 13 C values range from −2.88‰ to as low as −45.32‰, indicating hydrocarbon sources. These methods may provide a foundation upon which further hydrocarbon exploration techniques can develop.

Spectral studies of lunar equivalent rocks — A prelude to lunar mineral mapping

Hyperspectral remote sensing technique is widely applied for geological studies including the study of extra-terrestrial rocks. Since it has many spectral bands, discrimination between rocks and minerals can be done more precisely. To perform chemical and mineralogical mapping and to study the rocks on the lunar surface, India has proposed to launch its first lunar remote sensing satellite Chandrayaan-1 in the year 2008. For mineralogical mapping, the mission will carry a Hyperspectral Imager (HySI) instrument, which operates in the VNIR region. This paper presents-an attempt to study the spectral response of lunar-akin terrestrial rocks, in the VNIR region (as in the case of the proposed HySI on-board Chandrayaan-1). For this purpose, rocks similar to those present on the lunar surface were collected and their spectral response in the 64 simulated bands of HySI sensor were studied using a spectro-radiometer. Petrographic studies and modal analysis were carried out using thin sections of the rock samples. On studying the spectral response of the lunar-like rock samples in the 64 HySI bands, it is seen that there are distinct absorption features in bands 58 (923.75nm-927.5nm) and 63 (942.5nm-946.25nm) of the NIR wavelength ranges, for basalt rocks; distinct reflectance features in band 20 (590nm to 600nm) for ganmbbro: distinct reflectance features in band 19 (580nm to 590nm) and absorption in band 18 (570-580nm) for gabbroic anorthosite and distinct reflection features in band 63 (942.5nm to 946.25nm) for anorthosite. Thus, this study demonstrates the possibility of identifying the minerals and rocks on lunar surface using the hyperspectral approach and the spectral signatures of lunar-like rocks present on Earth.

Scene-based method for spatial misregistration detection in hyperspectral imagery

Hyperspectral imaging (HSI) sensors suffer from spatial misregistration, an artifact that prevents the accurate acquisition of the spectra. Physical considerations let us assume that the influence of the spatial misregistration on the acquired data depends both on the wavelength and on the across-track position. A scene-based method, based on edge detection, is therefore proposed. Such a procedure measures the variation on the spatial location of an edge between its various monochromatic projections, giving an estimation for spatial misregistration, and also allowing identification of misalignments. The method has been applied to several hyperspectral sensors, either prism, or grating-based designs. The results confirm the dependence assumptions on lambda and theta, spectral wavelength and across-track pixel, respectively. Suggestions are also given to correct for spatial misregistration.

Hyperspectral Fluorescence Imaging for Mouse Skin Tumor Detection

This paper presents a hyperspectral imaging technique based on laser-induced fluorescence for non-invasive detection of tumorous tissue on mouse skin. Hyperspectral imaging sensors collect image data in a number of narrow, adjacent spectral bands. Such high-resolution measurement of spectral information reveals contiguous emission spectra at each image pixel useful for the characterization of constituent materials. The hyperspectral image data used in this study are fluorescence images of mouse skin consisting of 21 spectral bands in the visible spectrum of the wavelengths ranging from 440 nm to 640 nm. Fluorescence signal is measured with the use of laser excitation at 337 nm. An acousto-optic tunable filter (AOTF) is used to capture images at 10 nm intervals. All spectral band images are spatially registered with the reference band image at 490 nm to obtain exact pixel correspondences by compensating the spatial offsets caused by the refraction differences in AOTF at different wavelengths during the image capture procedure. The unique fluorescence spectral signatures demonstrate a good separation to differentiate malignant tumors from normal tissues for rapid detection of skin cancers without biopsy.

Automatic target recognition for hyperspectral imagery using high-order statistics

Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective

Parametric adaptive signal detection for hyperspectral imaging

Hyperspectral imaging (HSI) sensors can provide very fine spectral resolution that allows remote identification of ground objects smaller than a full pixel in an HSI image. Traditional approaches to the so-called subpixel target signal detection problem are training inefficient due to the need for an estimate of a large-size covariance matrix of the background from target-free training pixels. This imposes a training requirement that is often difficult to meet in a heterogeneous environment. In this paper, a class of training-efficient adaptive signal detectors is presented by exploiting a parametric model that takes into account the nonstationarity of HSI data in the spectral dimension. A maximum-likelihood (ML) estimator is developed to estimate the parameters associated with the proposed parametric model. Several important issues are discussed, including model order selection, training screening, and time-series-based whitening and detection, which are intrinsic parts of the proposed parametric adaptive detectors. Experimental results using real HSI data reveal that the proposed parametric detectors are more training efficient and outperform conventional covariance-matrix-based detectors when the training size is limited

The development of Superspectral approaches for the improvement of land cover classification

This paper develops a critical review of the hyperspectral splitting of the solar reflected radiation acquired by hyperspectral imaging sensors. The bandwidth used in the range from 2.0-2.5 /spl mu/m by many hyperspectral sensors sometimes is too narrow for land cover classification. In fact, hyperspectral imagers often suffer from low signal-to-noise (SNR) in the shortwave infrared region of the electromagnetic spectrum, resulting in noisy image collection. This paper presents a new methodological approach to the splitting of the solar reflected radiation, called the It is based on the principle of increasing the channel bandwidth by increasing the number of wavelengths, to build synthetic spectral bands with higher SNR. The methodology has been applied to the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, an airborne hyperspectral scanner used for environmental remote sensing applications in Italy. Interesting results have been achieved in crop classification, processing the Cordenons survey carried out in August 2001 in the northeastern part of Italy. The Spectral Angle Mapper algorithm was used for classification because it is insensitive to shadows. For accuracy assessment, the overall accuracy (OA) and kappa coefficient (k) were calculated and used in the comparison. Using the superspectral approach, an increment in the overall accuracy of about 42% and an increment in the kappa coefficient of about 51% were obtained in comparison to the classification accuracy of unprocessed original MIVIS data (OA=41.21, k=0.35). A second case study is presented using the National Aeronautics and Space Administration's experimental hyperspectral imager HYPERION. Data acquired over the lake of Garda (Italy) in October 2002 was processed with the superspectral approach. Comparing the simulated HYPERION superspectral bands with the original data, SNR improvements are achieved in the shortwave infrared region (from 0.7-54.2 for 2.012-/spl mu/m wavelength and from 0.7-64.5 for the 2.365-/spl mu/m wavelength). The methodology proposed is sensor independent and can be applied to any of the hyperspectral sensors currently available.

A signal-decomposed and interference-annihilated approach to hyperspectral target detection

A hyperspectral imaging sensor can reveal and uncover targets with very narrow diagnostic wavelengths. However, it comes at a price that it can also extract many unknown signal sources such as background and natural signatures as well as unwanted man-made objects, which cannot be identified visually or a priori. These unknown signal sources can be referred to as interferers, which generally play a more dominant role than noise in hyperspectral image analysis. Separating such interferers from signals and annihilating them subsequently prior to detection may be a more realistic approach. In many applications, the signals of interest can be further divided into desired signals for which we want to extract and undesired signals for which we want to eliminate to enhance signal detectability. This paper presents a signal-decomposed and interference-annihilated (SDIA) approach in applications of hyperspectral target detection. It treats interferers and undesired signals as separate signal sources that can be eliminated prior to target detection. In doing so, a signal-decomposed interference/noise (SDIN) model is suggested in this paper. With the proposed SDIN model, the orthogonal subspace projection-based model and the signal/background/noise model can be included as its special cases. As shown in the experiments, the SDIN model-based SDIA approach generally can improve the performance of the commonly used generalized-likelihood ratio test and constrained energy minimization approach on target detection and classification.

An in‐scene method for atmospheric compensation of thermal hyperspectral data

Interpretation of surface radiation characteristics by overhead hyperspectral imagery often requires a compensation of the measured radiance data for the absorption and emission effects of the intervening atmosphere. We describe a procedure that accounts for these effects by direct use of the hyperspectral radiance data without recourse to ancillary meteorological data or atmospheric modeling. This in‐scene atmospheric compensation (ISAC) procedure is applicable to a broad class of problems. The present work concerns terrestrial surface characterization by remote sensing in the 8–13 μm atmospheric window band. A complete ISAC analysis is carried out in two steps. In the first step, unscaled atmospheric compensation spectra (transmittance and upwelling radiance) are extracted from the data. The step is carried out by application of a specially designed, line‐fitting procedure to a scatterplot constructed from the hyperspectral data. The compensation spectra are defined in terms of the slope and intercept parameters of the line. In the second step, these unscaled compensation spectra are scaled to quantitative compensation spectra. Here, this step is carried out using the strength of absorption in the 11.7‐μm water band. The compensation procedure is demonstrated by application to hyperspectral imagery obtained with the Aerospace Corporation's Spatially Enhanced Broadband Array Spectrograph System (SEBASS) hyperspectral imaging sensor at the Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) facility. Example applications demonstrate the ability of the method to remove atmospheric spectral structure from the observed data and reveal the spectral structure intrinsic to the underlying surface. This removal is demonstrated both for near‐blackbody surfaces composed of grass and for surfaces containing limestone gravel and Red Clay soil.

Synoptic Measurements of Chlorophyll- a and Suspended Particulate Matter in a Transitional Zone from Polluted to Clean Seawater Utilizing Airborne Remote Sensing and Ground Measurements, Haifa Bay (SE Mediterranean)

The hyperspectral Compact Airborne Spectrographic Imager (CASI) sensor was implemented to monitor water quality in a transitional zone from polluted to clean seawater, in Haifa Bay and adjacent river estuaries, at the northern part of the Mediterranean coast of Israel. Synoptic measurements of optical data acquired from the airborne scanner were used to map chlorophyll- a (chl- a) and suspended particulate matter (SPM) concentrations in surface waters in the study area. This airborne hyperspectral scanner was found as an expedient monitoring tool for the relatively small geographic area of the current study, as it enabled to reveal the patchy distribution, and sharp concentration changes of the mapped water characteristics. The distribution of SPM concentrations in Haifa Bay was mainly dictated by the polluted riverine inputs, with concentrations between 1 and 3 mg l −1 at its seaward border and higher by more than 1 order of magnitude at the river estuaries. The chl- a concentrations mapped and measured in this survey were unusually low (<2 μg l −1) due to a long-period intermission of anthropogenic phosphate and nitrate input to the bay. SPM and chl- a spatial distributions along the lower rivers system exhibit variations which could be plausibly explained by the hydrological structure and geochemical impacts on the riverine water sources. The correlation between SPM and some particulate heavy metal concentrations was found as a useful tool for monitoring such environmental hazardous substances.

Variability of Laboratory Measured Soil Lines of Soils from Southeastern Brazil

Linear relationships (soil lines) between the conventional red (R) and near-infrared (NIR), and between unconventional NIR reflectance bands were investigated for the effects of spectral positioning and widths of approximately simulated bands of some broad- and narrow-band sensors, and for the influence of the chemical constituents and moisture in the samples. The investigation was based on laboratory spectra of air-dried samples from soil profiles from southeastern Brazil, an experiment for the chemical removal of organic matter, and on a wetting process of the soil samples. The relationships were evaluated for a general soil line and for the individual lines of some soil types, under air-dried condition, through the determination of the line parameters, dispersion statistics, and principal components analysis (PCA). Soil lines obtained with broad or narrow NIR bands positioned at shorter wavelengths, and consequently at smaller distance differences between the pair of bands, presented better fitted lines than the ones obtained with NIR bands located at longer wavelengths and at larger distances. Compared with the broad bands, spectrally better positioned narrow NIR/R bands produced closely similar results because of the propitious effect of the larger bandwidth. However, unconventional NIR/NIR bands presented better fitted lines than the R/NIR bands. The inherent interplay between organic matter and iron oxides is accountable for the variability in the linear parameters of the individual lines. The general line for the samples considered is composed of nonparallel segments of the different soil types, with a tendency for an increased obliquity in the low albedo soils. Soil types with very low albedo present line slope values below 1, poor correlations, but small root mean square (RMS) values, whereas soil types with moderate and high albedo present line slope values above 1, strong correlations, and large RMS values. The former is rich in organic matter and iron oxides whereas the latter has more transparent components, such as quartz, or less opaque substances. However, in soil types with a lesser content of organic matter, the iron spectral features are enhanced and variations in the shape of the spectra are produced because of the larger reflectance increase in the R band than in the NIR interval. The maximum spectral contrast resulting from this compositional interplay is assessed when the NIR band is positioned at longer wavelengths. Similarly, soil wetting introduces nonlinear spectral changes that also affect the characteristics of the soil lines. The analysis of field measured reflectance spectra derived from an experiment designed to monitor the growth of a bean crop indicates that the shift of the NIR band towards shorter wavelengths produces not only better fitted soil lines but also stronger vegetation signals. The enhancement of the vegetation signal obtained with the displacement of the NIR band results from the steeper descending slope towards longer wavelengths of the NIR plateau observed in the vegetation spectra. The results place constraints on an unsupported choice of bands to derive vegetation indices, especially those of the advanced hyperspectral imaging sensors. The most recommended pair of NIR-R bands for crop vegetation indices is the one from LANDSAT-TM, closely followed by JERS-OPS and SPOT-HRV, or from narrow NIR-R bands positioned within these ranges at spectral intervals not affected by atmospheric attenuation.