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Geoscience and Remote Sensing, IEEE Transactions on

Issue 7 • Date July 2001

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Displaying Results 1 - 25 of 28
  • Introduction to the special issue on analysis of hyperspectral image data

    Page(s): 1343 - 1345
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    Freely Available from IEEE
  • Effects of the ground surface on polarimetric features of broadband radar scattering from subsurface metallic objects

    Page(s): 1556 - 1565
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    Throughout the world, the problem of buried unexploded ordnance (UXO) poses an enormous, persistent, and expensive problem. While UXO generally consists of sizable bodies of ferrous metal and can therefore be detected, with current technology it is extremely difficult to distinguish them reliably from typically widespread pieces of clutter. Thus the problem is one of subsurface discrimination. The authors previous modeling work on scattering of ground penetrating radar (GPR) from metallic objects surrounded by an infinite soil-like medium has suggested the utility of a number of key discriminants in broadband fully polarimetric sensing. In particular, resonance structure, induced field rotation and ellipticity, and bistatic observation of scattered signals were shown to offer key information about target shape and size. The authors investigate the effects on signature features of the proximity of a ground surface to the target, for the common case of shallow burial (<1 m). Overall, their analyses suggest that the key discriminants seen in scattering in an infinite medium survive the complex interactions with the ground surface. In some instances, these revealing signatures appear to be strengthened by the presence of a nearby surface. Multiposition backscatter also allows fundamental inferences about target elongation and symmetry when those cannot be obtained from single position viewing. View full abstract»

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  • Wind direction over the ocean determined by an airborne, imaging, polarimetric radiometer system

    Page(s): 1547 - 1555
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    The speed and direction of winds over the ocean can be determined by polarimetric radiometers. This has been established by theoretical work and demonstrated experimentally using airborne radiometers carrying out circle flights and thus measuring the full 360° azimuthal response from the sea surface. An airborne experiment, with the aim of measuring wind direction over the ocean using an imaging polarimetric radiometer, is described. A polarimetric radiometer system of the correlation type, measuring all four Stokes brightness parameters, is used. Imaging is achieved using a 1-m aperture conically scanning antenna. The polarimetric azimuthal signature of the ocean is known from modeling and circle flight experiments. Combining the signature with the measured brightness data from just a single flight track enables the wind direction to be determined on a pixel-by-pixel basis in the radiometer imagery View full abstract»

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  • Georegistration of airborne hyperspectral image data

    Page(s): 1347 - 1351
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    A suite of geometric sensor and platform modeling tools has been developed which have achieved consistent subpixel accuracy in orthorectification experiments. Aircraft platforms in turbulent atmospheric conditions present unique challenges and have required creative modeling approaches. The geometric relationship between an image point and a ground object has been modeled by rigorous photogrammetric methods. First and second order Gauss-Markov processes have been used to estimate the platform trajectory. These methods have been successfully applied to HYDICE and HyMap data sets. The most important contributors to the subpixel rectification accuracy have been the first order Gauss-Markov model with control linear features View full abstract»

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  • A new search algorithm for feature selection in hyperspectral remote sensing images

    Page(s): 1360 - 1367
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    A new suboptimal search strategy suitable for feature selection in very high-dimensional remote sensing images (e.g., those acquired by hyperspectral sensors) is proposed. Each solution of the feature selection problem is represented as a binary string that indicates which features are selected and which are disregarded. In turn, each binary string corresponds to a point of a multidimensional binary space. Given a criterion function to evaluate the effectiveness of a selected solution, the proposed strategy is based on the search for constrained local extremes of such a function in the above-defined binary space. In particular, two different algorithms are presented that explore the space of solutions in different ways. These algorithms are compared with the classical sequential forward selection and sequential forward floating selection suboptimal techniques, using hyperspectral remote sensing images (acquired by the airborne visible/infrared imaging spectrometer [AVIRIS] sensor) as a data set. Experimental results point out the effectiveness of both algorithms, which can be regarded as valid alternatives to classical methods, as they allow interesting tradeoffs between the qualities of selected feature subsets and computational cost View full abstract»

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  • A spectral mixture process conditioned by Gibbs-based partitioning

    Page(s): 1421 - 1434
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    An enhanced method of spectral mixture analysis is investigated for hyperspectral imagery of moderate-to-high scene complexity, where either a large set of fundamental materials may exist throughout, or where some of the fundamental members have spectra that are similar to each other. For a complex scene, the use of one large set of fundamental materials as the set of “endmembers” for performing spectral unmixing can cause unreliable estimates of material compositions at sites within the scene. In such cases, partitioning this large set of endmembers into a number of smaller sets is appropriate, where the smaller sets are associated with certain regions in a scene. Herein, a Gibbs-based algorithm is developed to partition hyperspectral imagery into regions of similarity. This partitioning algorithm provides an estimator of an underlying and unobserved process called a “partition process” that coexists with other underlying (and unobserved) processes, one of which is called a “spectral mixing process.” The algorithm exploits the properties of a Markov random field (MRF) and the associated Gibbs equivalence theorem, using a suitably defined graph structure and a Gibbs distribution to model the partition process. Consequently, spatial consistency is imposed on the spectral content of sites in each partition. The enhanced spectral mixing process is then computed as a linear mixture model that is conditioned on the partition process. Experiments are performed using scenes of HYDICE imagery to validate the algorithm, where spectral mixture analysis is performed with and without conditioning on the partitioning process View full abstract»

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  • Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks

    Page(s): 1508 - 1524
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    The authors present a new methodology for estimating the concentration of sea water optically active constituents from remotely sensed hyperspectral data, based on generalized radial basis function neural networks (GRBF-NNs). This family of NNs is particularly suited to approximate relationships like those between hyperspectral reflectance data and the concentrations of optically active constituents of the water body, which are highly nonlinear, especially in case II waters. Three main water constituents are taken into account: phytoplankton, nonchlorophyllous particles, and yellow substance. Each parameter is estimated by means of a specific multi-input single-output GRBF-NN. The authors adopt a recently proposed network learning strategy based on the combined use of the regression tree procedure and forward selection. The effectiveness of this approach, which is completely general and can be easily applied to any hyperspectral sensor, is proved using data simulated with an ocean color model over the channels of the medium resolution imaging spectrometer (MERIS), the new generation ESA sensor to be launched in 2001. The authors define the estimation algorithms over waters of cases I, II, and I+II and compare their performance with that of classical band-ratio, single-band, and multilinear algorithms. Generally, the GRBF-NN algorithms outperform the classical ones, except for the multilinear over case I waters. A particular improvement Is over case II waters, where the mean square error (MSE) can be reduced by one or two orders of magnitude over the error of multilinear and band-ratio algorithms, respectively View full abstract»

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  • Spectral mixture analysis of simulated thermal infrared spectrometry data: An initial temperature estimate bounded TESSMA search approach

    Page(s): 1435 - 1446
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    At sensor thermal infrared (TIR) radiation varies depending on the temperature and emissivity of surface materials and the modifying impact of atmospheric absorption and emission. TIR imaging spectrometry often involves extracting temperature, emissivity, and/or surface composition, which are useful in diverse studies ranging from climatology to land use analyses. A two-stage application of temperature emissivity separation (TES) using spectral mixture analysis (SMA) or TESSMA, was employed to characterize isothermal mixtures on a subpixel basis. This two-stage approach first uses the relationship between a virtual cold endmember fraction and surface temperature to extract initial image temperature estimates. Second, an isothermal SMA application searches the region within the maximum temperature error range of the initial estimate, selecting the best subpixel spectral mixture fit. Work presented includes characterizations of synthetically generated temperature and constituent mixture gradient test images, and a discussion of errors associated with selecting temperature search ranges 25% and 75% smaller than the initial temperature calculation error range. Results using this two-stage approach indicate improved overall temperature estimates, constituent estimates, and constituent fraction estimates using simulated TIR data View full abstract»

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  • A Bayesian classification model for sea ice roughness from scatterometer data

    Page(s): 1586 - 1595
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    For sea ice in the Baltic Sea, surface scattering can be regarded as the dominant scattering mechanism at C-band. In this paper, a new statistical method is introduced for making statistical inferences about the underlying ice surface roughness on the basis of one-dimensional (1D) scatterometer data y. The central parameter in the hierarchical model applied in the context is a mixture parameter p, which indicates the degree of surface roughness in ice surface. Several questions related to the occurrence of different ice classes on a transect can be solved with the aid of the posterior distribution [p|y]. An empirical approximation for the posterior distribution is computed by using Markov Chain Monte Carlo methodology. The efficiency of the suggested approach is investigated by analyzing a C-band HH-polarization helicopter-borne HUTSCAT scatterometer data. The results provided by the statistical model show good agreement with a video-based ice type classification View full abstract»

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  • Effect of lossy vector quantization hyperspectral data compression on retrieval of red-edge indices

    Page(s): 1459 - 1470
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    This paper evaluates lossy vector quantization-based hyperspectral data compression algorithms, using red-edge indices as end-products. Three compact airborne spectrographic imager (CASI) data sets and one airborne visible/infrared imaging spectrometer (AVIRIS) data set from vegetated areas were tested. A basic compression system for hyperspectral data called the “reference” system, and three-speed improved compression systems called systems 1, 2, and 3, respectively, were examined. Five red-edge products representing the near infrared (NIR) reflectance shoulder (Vog 1), the NIR reflectance maximum (Red rs), the difference between the reflectance maximum and the minimum (Red rd), the wavelength of the reflectance maximum (Red lo), and the wavelength of the point of inflection of the NIR vegetation reflectance curve (Red lp) were retrieved from each original data set and from their decompressed data sets. The experiments show that the reference system induces the smallest product errors of the four compression systems. System 1 and 2 perform fairly closely to the reference system. They are the recommended compression systems since they compress a data set hundreds of times faster than the reference system. System 3 performs similarly to the reference system at high compression ratios. Product errors increase with the increase of compression ratio. The overall product errors are dominated by Vog 1, Red rs, and Red rd, since the amplitude of product error for these products is over one order of magnitude greater than those for the Red-lo and Red lp products. The difference between the overall error from the reference and that from system 1 or 2 is below 0.5% at all compression ratios. The overall product error induced by system 1 or 2 is below 3.0% and 2.0% for CASI and AVIRIS data sets, respectively, when the compression ratio is 100 and below. Spatial patterns of the product errors were examined for tha AVRIS data set. For all products, the errors are uniformly distributed in vegetated areas. Errors are relatively high in nonvegeted and mixed-pixel areas View full abstract»

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  • The impact of viewing geometry on material discriminability in hyperspectral images

    Page(s): 1352 - 1359
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    An increase in the off-nadir viewing angle for an airborne visible/near-infrared through short-wave infrared (VNIR/SWIR) imaging spectrometer leads to a decrease in upward atmospheric transmittance and an increase in line-of-sight scattered path radiance. These effects combine to reduce the spectral contrast between different materials in the sensed signal. The authors analyze the impact of viewing angle on material discriminability for 237 materials over a wide range of conditions. Material discriminability is quantified using a statistical algorithm that employs a subspace model to represent the set of spectra for a material as conditions vary. The authors show that reliable material discrimination is possible over a range of conditions even for large off-nadir viewing angles. They illustrate the performance of material identification over different viewing angles using simulated forest and desert hyperspectral digital imagery collection experiment (HYDICE) images View full abstract»

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  • Automated differentiation of urban surfaces based on airborne hyperspectral imagery

    Page(s): 1525 - 1532
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    The urban environment is characterized by an intense use of the available space, where the preservation of open green spaces is of special ecological importance. Because of dynamic urban development and high mapping costs, municipal authorities are interested in effective methods for mapping urban surface cover types that can be used for evaluating ecological conditions in urban structures and supporting updates of biotope mapping. Against this background, airborne hyperspectral remote sensing data of the DAIS 7915 instrument have been analyzed for their potential in automated area-wide differentiation of ecologically meaningful urban surface cover types for a study area in the city of Dresden, Germany. The small urban structures and the high spectral information content of the hyperspectral image data require the development of special methods capable of dealing with the resulting large number of mixed pixels. In this paper, a new approach is presented that combines advantages of classification with linear spectral unmixing. Since standard unmixing techniques are not suitable for an area-wide analysis of urban surfaces representing a large number of spectrally similar endmembers (EMs), the mathematical model, were extended and a new method for pixel-oriented EM selection was developed. This method reduces the number of possible EM combination for each pixel by introducing spectrally pure seedlings and a list of possible EM combinations into a neighborhood-oriented iterative unmixing procedure. The results and their comparison with standard spectral classification methods show that the new pixel- and contest-based approach enables reasonable material-oriented differentiation of urban surfaces View full abstract»

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  • Wavelets for computationally efficient hyperspectral derivative analysis

    Page(s): 1540 - 1546
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (112 KB) |  | HTML iconHTML  

    Smoothing followed by a derivative operation is often used in the analysis of hyperspectral signatures. The width of the smoothing and/or derivative operator can greatly affect the utility of the method. If one is unsure of the appropriate width or would like to conduct analysis for several widths, scale-space images can be used. This paper shows how the wavelet transform modulus-maxima method can be used to formalize and generalize the smoothing followed by derivative analysis and how the wavelet transform ran be used to greatly decrease computational costs of the analysis. The Mallat/Zhong wavelet algorithm is compared to the traditional method, convolution with Gaussian derivative filters, for computing scale-space images. Both methods are compared on two points: (1) computational expense and (2) resulting scalar decompositions. The results show that the wavelet algorithm can greatly reduce the computational expense while practically no differences exist in the subsequent scaler decompositions. The analysis is conducted on a database of hyperspectral signatures, namely, hyperspectral digital image collection experiment (HYDICE) signatures. The reduction in computational expense is by a factor of about 30, and the average Euclidean distance between resulting scale-space images is on the order of 0.02 View full abstract»

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  • Aerosol optical characteristics from a summer campaign in an urban coastal Mediterranean area

    Page(s): 1573 - 1585
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    The authors present a preliminary study of some optical properties of atmospheric aerosols over the area of Valencia, Spain, a coastal Mediterranean city. Measurements of spectral direct irradiance in the 300-1100 nm range were taken simultaneously at three sites: rural-continental, rural-coastal, and urban-coastal, all located within a 50 km radius of the city of Valencia. The irradiance measurements were obtained using three Li-cor 1800 spectroradiometers provided with radiance limiting tubes with field of views (FOVs) of 4.7°. The measurements were made under clear sky conditions during a field campaign carried out in the summer of 1998. In order to avoid the uncertainties associated with the determination of the water vapor content and the other atmospheric constituents, the analysis of the spectral aerosol optical thickness (AOT) values was limited to the 400-670 mm spectral band. From the values of the spectral AOT, both the Angstrom coefficients and the aerosol size distributions were obtained. The results show the great dependence of the optical aerosol characteristic on the direction of the prevailing winds (maritime or continental) in this area View full abstract»

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  • A digital-analog hybrid technique for low range sidelobe pulse compression

    Page(s): 1612 - 1615
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    Pulse compression using a digital-analog hybrid technique is studied to achieve very low range sidelobes for potential application to spaceborne rain radar. Range sidelobe suppression of 60 dB is attained in both calculation and test measurement by optimizing the transmission signal to cancel the defects in pulse compression process View full abstract»

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  • Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery

    Page(s): 1410 - 1420
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    The use of matched filters on hyperspectral data has made it possible to detect faint signatures. This study uses a modified k-means clustering to improve matched filter performance. Several simple bivariate cases are examined in detail, and the interaction of filtering and partitioning is discussed. The authors show that clustering can reduce within-class variance and group pixels with similar correlation structures. Both of these features improve filter performance. The traditional k-means algorithm is modified to work with a sample of the image at each iteration and is tested against two hyperspectral datasets. A new “extreme” centroid initialization technique is introduced and shown to speed convergence. Several matched filtering formulations (the simple matched filter, the clutter matched filter, and the saturated matched filter) are compared for a variety of number of classes and synthetic hyperspectral images. The performance of the various clutter matched filter formulations is similar, all are about an order of magnitude better than the simple matched filter. Clustering is found to improve the performance of all matched filter formulations by a factor of two to five. Clustering in conjunction with clutter matched filtering can improve fifty-fold over the simple case, enabling very weak signals to be detected in hyperspectral images View full abstract»

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  • Change detection for linear features in aerial photographs using edge-finding

    Page(s): 1608 - 1612
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    The authors describe a system fur automatic change detection of linear features such as roads and buildings in aerial photographs. Rather than compare pixels, they match major line segments and note those without counterparts. Experiments show their methods to be promising for images of around 2-m resolution View full abstract»

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  • Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery

    Page(s): 1471 - 1479
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    Hyperspectral imagery from the airborne multispectral scanner was evaluated for discrimination and mapping of vegetation components in a semi-arid rangeland environment in Southern Australia. Automated unmixing of two image strips with 5-m resolution revealed several vegetation endmembers in the visible, near infrared (near-IR), and short-wave infrared portions of the imagery. Identity of the endmembers was determined through examination of their short-wave infrared and full-wavelength spectra, and their mapped distributions and correlation with percent cover of vegetation species were measured in sample plots. In addition, to assist interpretation of the image signatures, short-wave infrared reflectance spectra for the dominant vegetation components at the study site were collected with a portable infrared mineral analyzer (PIMA) spectrometer. Endmembers separately mapped included Eucalyptus and other trees such as sugarwood, understorey chenopod shrubs, dry plant litter, and soil surface cryptogamic crust. Several endmembers were significantly positively correlated with field measurements of plant cover. Most of the tree canopy endmembers showed broad cellulose-lignin absorption features in the short-wave infrared (SWIR), and narrower absorptions caused by plant waxes and oils. The field spectra confirmed that the sclerophyll and xerophytic plants show identifiable cellulose, lignin, and plant wax absorption features, even when live and actively photosynthesising. This spectral expression of biochemical constituents in live plants points to the benefit of using the whole spectral range from visible to short-wave infrared in vegetation studies View full abstract»

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  • Best-bases feature extraction algorithms for classification of hyperspectral data

    Page(s): 1368 - 1379
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    Due to advances in sensor technology, it is now possible to acquire hyperspectral data simultaneously in hundreds of bands. Algorithms that both reduce the dimensionality of the data sets and handle highly correlated bands are required to exploit the information in these data sets effectively. the authors propose a set of best-bases feature extraction algorithms that are simple, fast, and highly effective for classification of hyperspectral data. These techniques intelligently combine subsets of adjacent bands into a smaller number of features. Both top-down and bottom-up algorithms are proposed. The top-down algorithm recursively partitions the bands into two (not necessarily equal) sets of bands and then replaces each final set of bands by its mean value. The bottom-up algorithm builds an agglomerative tree by merging highly correlated adjacent bands and projecting them onto their Fisher direction, yielding high discrimination among classes. Both these algorithms are used in a pairwise classifier framework where the original C-class problem is divided into a set of (2C) two-class problems. The new algorithms (1) find variable length bases localized in wavelength, (2) favor grouping highly correlated adjacent bands that, when merged either by taking their mean or Fisher linear projection, yield maximum discrimination, and (3) seek orthogonal bases for each of the (2C) two-class problems into which a C-class problem can be decomposed. Experiments on an AVIRIS data set for a 12-class problem show significant improvements in classification accuracies while using a much smaller number of features View full abstract»

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  • On the local minima in a tomographic imaging technique

    Page(s): 1596 - 1607
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    The reliability of a recently introduced nonlinear estimation method for tomographic imaging is discussed in full detail. It is shown how a proper choice of the functional spaces to which unknown quantities belong and the exploitation of the expected properties of the object under test and of all the available a priori information positively affect robustness against false solutions of the inversion procedure. The developed arguments allow the authors to understand causes of possible false solutions, suggesting possible countermeasures. In particular, it is shown how the proposed approach allows the authors to achieve accurate and reliable reconstructions in a set of cases larger than the range of applicability of other “false solutions free” approaches. Numerical analyses confirm the validity of the approach and the effectiveness of developed inversion procedures through practical examples View full abstract»

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  • A two-dimensional Doppler-Radiometer for Earth observation

    Page(s): 1566 - 1572
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    Compared to synthetic aperture radars (SARs), the angular resolution of microwave radiometers is quite poor. Traditionally, it has been limited by the physical size of the antenna. However, the angular resolution can be improved by means of aperture synthesis interferometric techniques. A narrow beam is synthesized during the image formation processing of the cross-correlations measured at zero-lag between pairs of signals collected by an array of antennas. The angular resolution is then determined by the maximum antenna spacing normalized to the wavelength (baseline). The next step in improving the angular resolution is the Doppler-Radiometer, somehow related to the super-synthesis radiometers and the Radiometer-SAR. This paper presents the concept of a three-antenna Doppler-Radiometer for 2D imaging. The performance of this instrument is evaluated in terms of angular/spatial resolution and radiometric sensitivity, and an L-band illustrative example is presented View full abstract»

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  • Hyperspectral subpixel target detection using the linear mixing model

    Page(s): 1392 - 1409
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    Relative to multispectral sensing, hyperspectral sensing can increase the detectability of pixel and subpixel size targets by exploiting finer detail in the spectral signatures of targets and natural backgrounds. Over the past several years, different algorithms for the detection of full-pixel or subpixel targets with known spectral signature have been developed. The authors take a closer and more in-depth look at the class of subpixel target detection algorithms that explore the linear mixing model (LMM) to characterize the targets and the interfering background. Sensor noise is modeled as a Gaussian random vector with uncorrelated components of equal variance. The paper makes three key contributions. First, it provides a complete and self-contained theoretical derivation of a subpixel target detector using the generalized likelihood ratio test (GLRT) approach and the LMM. Some other widely used algorithms are obtained as byproducts. The performance of the resulting detector, under the postulated model, is discussed in great detail to illustrate the effects of the various operational factors. Second, it introduces a systematic approach to investigate how well the adopted model characterizes the data, and how robust the detection algorithm is to model-data mismatches. Finally, it compares the derived algorithms with regard to two desirable properties: capacity to operate in constant false alarm rate mode and ability to increase the separation between target and background View full abstract»

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  • Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data

    Page(s): 1491 - 1507
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    Radiative transfer theory and modeling assumptions were applied at laboratory and field scales in order to study the link between leaf reflectance and transmittance and canopy hyper-spectral data for chlorophyll content estimation. This study was focused on 12 sites of Acer saccharum M. (sugar maple) in the Algoma Region, Canada, where field measurements, laboratory-simulation experiments, and hyper-spectral compact airborne spectrographic imager (CASI) imagery of 72 channels in the visible and near-infrared region and up to 1-m spatial resolution data were acquired in the 1997, 1998, and 1999 campaigns. A different set of 14 sites of the same species were used in 2000 for validation of methodologies. Infinite reflectance and canopy reflectance models were used to link leaf to canopy levels through radiative transfer simulation. The closed and dense (LAI>4) forest canopies of Acer saccharum M. used for this study, and the high spatial resolution reflectance data targeting crowns, allowed the use of optically thick simulation formulae and turbid-medium SAILH and MCRM canopy reflectance models for chlorophyll content estimation by scaling-up and by numerical model inversion approaches through coupling to the PROSPECT leaf radiative transfer model. Study of the merit function in the numerical inversion showed that red edge optical indices used in the minimizing function such as R750/R710 perform better than when all single spectral reflectance channels from hyper-spectral airborne CASI data are used, and in addition, the effect of shadows and LAI variation are minimized View full abstract»

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  • Unsupervised target detection in hyperspectral images using projection pursuit

    Page(s): 1380 - 1391
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (340 KB) |  | HTML iconHTML  

    The authors present a projection pursuit (PP) approach to target detection. Unlike most of developed target detection algorithms that require statistical models such as linear mixture, the proposed PP is to project a high dimensional data set into a low dimensional data space while retaining desired information of interest. It utilizes a projection index to explore projections of interestingness. For target detection applications in hyperspectral imagery, an interesting structure of an image scene is the one caused by man-made targets in a large unknown background. Such targets can be viewed as anomalies in an image scene due to the fact that their size is relatively small compared to their background surroundings. As a result, detecting small targets in an unknown image scene is reduced to finding the outliers of background distributions. It is known that “skewness,” is defined by normalized third moment of the sample distribution, measures the asymmetry of the distribution and “kurtosis” is defined by normalized fourth moment of the sample distribution measures the flatness of the distribution. They both are susceptible to outliers. So, using skewness and kurtosis as a base to design a projection index may be effective for target detection. In order to find an optimal projection index, an evolutionary algorithm is also developed to avoid trapping local optima. The hyperspectral image experiments show that the proposed PP method provides an effective means for target detection View full abstract»

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  • Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California

    Page(s): 1480 - 1490
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    Ecosystem responses to interannual weather variability are large and superimposed over any long-term directional climatic responses making it difficult to assign causal relationships to vegetation change. Better understanding of ecosystem responses to interannual climatic variability is crucial to predicting long-term functioning and stability. Hyperspectral data have the potential to detect ecosystem responses that are undetected by broadband sensors and can be used to scale to coarser resolution global mapping sensors, e.g., advanced very high resolution radiometer (AVHRR) and MODIS. This research focused on detecting vegetation responses to interannual climate using the airborne visible-infrared imaging spectrometer (AVIRIS) data over a natural savanna in the Central Coast Range in California. Results of linear spectral mixture analysis and assessment of the model errors were compared for two AVIRIS images acquired in spring of a dry and a wet year. The results show that mean unmixed fractions for these vegetation types were not significantly different between years due to the high spatial variability within the landscape. However, significant community differences were found between years on a pixel basis, underlying the importance of site-specific analysis. Multitemporal hyperspectral coverage is necessary to understand vegetation dynamics View full abstract»

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.

 

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Editor-in-Chief
Antonio J. Plaza
University of Extremadura