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Geoscience and Remote Sensing, IEEE Transactions on

Issue 7  Part 2 • Date July 2009

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Displaying Results 1 - 25 of 33
  • [Front cover]

    Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Page(s): C2
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    Freely Available from IEEE
  • Table of contents

    Page(s): 2089 - 2090
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    Freely Available from IEEE
  • Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction

    Page(s): 2091 - 2105
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2014 KB) |  | HTML iconHTML  

    This paper proposes an innovative spectral feature extraction (SFE) method called prototype space (PS) feature extraction (PSFE) based only on class spectra. The main novelties of the proposed SFE lie in the following: representing channels in a new space called PS, where they are characterized in terms of reflection properties of classes; and proposing uncertainty, angle, and distance measures to distinguish highly correlated and informative channels. Having clustered the channels by Fuzzy C-Means (FCM) in PS, highly correlated and isolated channels are separated by an uncertainty measure. Consequently, PSFE is built by a linear combination of spectra weighted by class membership values of channels that fall in each cluster. Furthermore, we will enrich PSFE with informative channels which are outlier channels identified through their angle and distance with respect to diagonal and cluster centers in PS. In contrast to the previous SFE methods, PSFE substitutes the search strategies with FCM clustering to find relevant channels. Moreover, instead of optimizing separability criteria, the accuracy of classification over a subset of training data is used to decide which disjoint optical region yields maximum accuracy. According to how class spectra are obtained, PSFE incorporates four approaches: knowledge based, supervised, semisupervised, and unsupervised. The latter three PSFE approaches are assessed in two main cases including with and without informative channels and compared with the conventional feature extraction methods. Experimental results demonstrated higher overall accuracy of PSFE compared to its conventional counterparts with limited sample sizes. View full abstract»

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  • A Matched-Filter-Bank-Based 3-D Imaging Algorithm for Rapidly Spinning Targets

    Page(s): 2106 - 2113
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (668 KB) |  | HTML iconHTML  

    For rapidly spinning targets, such as the rotating ground radar antenna, helicopter blades, spinning space debris, etc., the scatterers on the target may rotate for several periods in the observation time. Since the range and Doppler information of these scatterers are no longer constant, the conventional range-Doppler-based imaging algorithms are invalid. Meanwhile, 3-D imaging is necessary to obtain additional information for the spinning target. However, the available interferometric inverse synthetic radar (ISAR) and snapshot 3-D imaging algorithms do not work well since they require low target spinning speed. In this paper, a matched-filter-bank-based 3-D imaging algorithm for rapidly spinning targets is proposed, based on target motion features. This algorithm utilizes the rapidly rotating turntable model of the ISAR target instead of the slow rotating one. First, 2-D image slices of the target are obtained from the output of the matched filter bank by changing matching parameters. Then, a series of 2-D image slices are combined to form the 3-D target image. Since this algorithm applies to the monostatic radar system, it is easy to implement in practical applications. Both the theoretical derivation and the simulation results have proved the validity of the proposed algorithm. View full abstract»

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  • Unsupervised Change Detection From Multichannel SAR Data by Markovian Data Fusion

    Page(s): 2114 - 2128
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (731 KB) |  | HTML iconHTML  

    In applications related to environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a great potential, owing both to their insensitivity to atmospheric and Sun-illumination conditions and to the improved discrimination capability they may provide as compared with single-channel SAR. However, exploiting this potential requires accurate and automatic techniques to generate change maps from (multichannel) SAR images acquired over the same geographic region in different polarizations or at different frequencies at different times. In this paper, a contextual unsupervised change-detection technique (based on a data-fusion approach) is proposed for two-date multichannel SAR images. Each SAR channel is modeled as a distinct information source, and a Markovian approach to data fusion is adopted. A Markov random field model is introduced that combines together the information conveyed by each SAR channel and the spatial contextual information concerning the correlation among neighboring pixels and formulated by using ldquoenergy functions.rdquo In order to address the task of the estimation of the model parameters, the expectation-maximization algorithm is combined with the recently proposed ldquomethod of log-cumulants.rdquo The proposed technique was experimentally validated with semisimulated multipolarization and multifrequency data and with real SIR-C/XSAR images. View full abstract»

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  • Hierarchical Texture-Based Segmentation of Multiresolution Remote-Sensing Images

    Page(s): 2129 - 2141
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1634 KB) |  | HTML iconHTML  

    In this paper, we propose a new algorithm for the segmentation of multiresolution remote-sensing images, which fits into the general split-and-merge paradigm. The splitting phase singles out clusters of connected regions that share the same spatial and spectral characteristics. These clusters are then regarded as atomic elements of more complex structures, particularly textures, that are gradually retrieved during the merging phase. The whole process is based on a recently developed hierarchical model of the image, which accurately describes its textural properties. In order to reduce the computational burden and preserve contours at the highest spatial definition, the algorithm works on the high-resolution panchromatic data first, using low-resolution full spectral information only at a later stage to refine the segmentation. It is completely unsupervised, with just a few parameters set at the beginning, and its final product is not a single segmentation map but rather a sequence of nested maps which provide a hierarchical description of the image, at various scales of observations. The first experimental results, obtained on a remote-sensing Ikonos image, are very encouraging and confirm the algorithm potential. View full abstract»

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  • A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples

    Page(s): 2142 - 2154
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1737 KB) |  | HTML iconHTML  

    This paper presents a novel context-sensitive semisupervised support vector machine (CS4VM) classifier, which is aimed at addressing classification problems where the available training set is not fully reliable, i.e., some labeled samples may be associated to the wrong information class (mislabeled patterns). Unlike standard context-sensitive methods, the proposed CS4VM classifier exploits the contextual information of the pixels belonging to the neighborhood system of each training sample in the learning phase to improve the robustness to possible mislabeled training patterns. This is achieved according to both the design of a semisupervised procedure and the definition of a novel contextual term in the cost function associated with the learning of the classifier. In order to assess the effectiveness of the proposed CS4VM and to understand the impact of the addressed problem in real applications, we also present an extensive experimental analysis carried out on training sets that include different percentages of mislabeled patterns having different distributions on the classes. In the analysis, we also study the robustness to mislabeled training patterns of some widely used supervised and semisupervised classification algorithms (i.e., conventional support vector machine (SVM), progressive semisupervised SVM, maximum likelihood, and k-nearest neighbor). Results obtained on a very high resolution image and on a medium resolution image confirm both the robustness and the effectiveness of the proposed CS4VM with respect to standard classification algorithms and allow us to derive interesting conclusions on the effects of mislabeled patterns on different classifiers. View full abstract»

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  • Development and Testing of a Subpixel Mapping Algorithm

    Page(s): 2155 - 2164
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1751 KB) |  | HTML iconHTML  

    This paper forwards an advanced subpixel mapping algorithm to provide detailed information on the spatial distribution of land covers within a mixed pixel. This is achieved by utilizing the area proportions of the endmember components of a mixed pixel and its neighboring pixels. Fraction values, obtained through soft classification, are used to calculate the area ratios of the endmember components of the mixed pixel and the neighboring pixels. After testing the algorithm with both artificial and synthetic images, the performance of the algorithm can be evaluated as being computationally efficient and accurate for obtaining comprehensive information on the spatial distribution of land covers. View full abstract»

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  • Generalization of Subpixel Analysis for Hyperspectral Data With Flexibility in Spectral Similarity Measures

    Page(s): 2165 - 2171
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (460 KB) |  | HTML iconHTML  

    Several spectral unmixing techniques have been developed for subpixel mapping using hyperspectral data in the past two decades, among which the fully constrained least squares method based on the linear spectral mixture model (LSMM) has been widely accepted. However, the shortage of this method is that the Euclidean spectral distance measure is used, and therefore, it is sensitive to the magnitude of the spectra. While other spectral matching criteria are available, such as spectral angle mapping (SAM) and spectral information divergence (SID), the current unmixing algorithm is unable to be extended to these measures. In this paper, we propose a unified subpixel mapping framework that models the unmixing process as a best match of the unknown pixel's spectrum to a weighted sum of the endmembers' spectra. We introduce sequential quadratic programming to solve the nonlinear optimization problem encountered in the implementation of this framework. The main feature of this proposed method is that it is not restricted to any particular similarity measures. Experiments were conducted with both simulated and Hyperion data. The tests demonstrated the proposed framework's advantage in accommodating various spectral similarity measures and provided performance comparisons of the Euclidean distance measure with other spectral matching criteria including SAM, spectral correlation measure, and SID. View full abstract»

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  • Automatic Ground-Truth Validation With Genetic Algorithms for Multispectral Image Classification

    Page(s): 2172 - 2181
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (943 KB) |  | HTML iconHTML  

    In this paper, we propose a novel method that aims at assisting the ground-truth expert through an automatic detection of potentially mislabeled learning samples. This method is based on viewing the mislabeled sample detection issue as an optimization problem where it is looked for the best subset of learning samples in terms of statistical separability between classes. This problem is formulated within a genetic optimization framework, where each chromosome represents a candidate solution for validating/invalidating the learning samples collected by the ground-truth expert. The genetic optimization process is guided by the joint optimization of two different criteria which are the maximization of a between-class statistical distance and the minimization of the number of invalidated samples. Experiments conducted on both simulated and real data sets show that the proposed ground-truth validation method succeeds in the following: 1) in detecting the mislabeled samples with a high accuracy, even when up to 30% of the learning samples are mislabeled, and 2) in strongly limiting the negative impact of the mislabeling issue on the accuracy of the classification process. View full abstract»

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  • Spatial-Resolution Enhancement of SMOS Data: A Deconvolution-Based Approach

    Page(s): 2182 - 2192
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1759 KB) |  | HTML iconHTML  

    A deconvolution-based model has been developed in an attempt to improve the spatial resolution of future soil moisture and ocean salinity (SMOS) data. This paper is devoted to the analysis and evaluation of different algorithms using brightness temperature images obtained from an upgraded version of the SMOS end-to-end performance simulator. Particular emphasis is made on the use of least-square-derived Lagrangian methods on the Fourier and wavelet domains. The possibility of adding suitable auxiliary information in the reconstruction process has also been addressed. Results indicate that, with these techniques, it is feasible to enhance the spatial resolution of SMOS observations by a factor of 1.75 while preserving the radiometric sensitivity simultaneously. View full abstract»

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  • Using Stacked Generalization to Combine SVMs in Magnitude and Shape Feature Spaces for Classification of Hyperspectral Data

    Page(s): 2193 - 2205
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1039 KB) |  | HTML iconHTML  

    This paper proposes to improve the classification accuracy of hyperspectral data with support vector machines (SVMs) by using stacked generalization (stacking) as well as the complementary information of magnitude and shape feature spaces. Stacking is a method to combine multiple classifiers by learning a meta-level (or level-1) classifier from the outputs of base-level (or level-0) classifiers (estimated via cross-validation). In the processing of hyperspectral data, magnitude features are the radiance values at different sensor bands, whereas shape features are the differences in direction rather than the magnitude of the spectral signatures. In particular, the proposed method is as follows: (1) SVMs trained in magnitude and shape feature spaces are adopted as level-0 classifiers (termed as level-0 SVMs); (2) outputs (decision values) of the level-0 SVMs are used as inputs (termed as meta-level features) of level-1 classifier, since the decision values contain much more information than class labels; (3) level-1 classifier adopts SVMs (level-1 SVMs) trained in the meta-level feature space. In addition, we also discuss the possibility of reducing the number of level-0 SVMs by meta-level feature selection and present one simple solution. Experiments on a benchmark hyperspectral data set demonstrate that our method significantly outperforms the methods with the single feature space and other combining methods, namely, simple voting, absolute maximum decision value, and stacking with class labels. View full abstract»

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  • Automatic Analysis of GPR Images: A Pattern-Recognition Approach

    Page(s): 2206 - 2217
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1568 KB) |  | HTML iconHTML  

    In this paper, we propose a novel pattern-recognition system to identify and classify buried objects from ground-penetrating radar (GPR) imagery. The entire process is subdivided into four steps. After a preprocessing step, the GPR image is thresholded to put under light the regions containing potential objects. The third step of the system consists of automatically detecting the objects in the obtained binary image by means of a search of linear/hyperbolic patterns formulated within a genetic optimization framework. In the genetic optimizer, each chromosome models the apex position and the curvature associated with the candidate pattern, while the fitness function expresses the Hamming distance between that pattern and the binary image content. Finally, in the fourth step, the problem of the recognition of the material type of the identified objects is approached as a classification issue, which is solved by means of an opportune feature-extraction strategy and a support vector machine classifier. To illustrate the performances of the proposed system, we conducted a thorough experimental study based on GPR images generated by a GPR simulator based on the finite-difference time-domain method so as to construct different acquisition scenarios by varying the number of buried objects, their position, their size, their shape, and their material type. In general, the obtained experimental results show that the proposed system exhibits promising performances both in terms of object detection and material recognition. View full abstract»

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  • Active Learning Methods for Remote Sensing Image Classification

    Page(s): 2218 - 2232
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2693 KB) |  | HTML iconHTML  

    In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed. View full abstract»

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  • Intercomparison of Digital Fast Fourier Transform and Acoustooptical Spectrometers for Microwave Radiometry of the Atmosphere

    Page(s): 2233 - 2239
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (564 KB) |  | HTML iconHTML  

    The Institute of Applied Physics (IAP) of the University of Bern is active in the field of remote sensing of middle atmospheric trace gases such as ozone and water vapor by microwave radiometry. From the measured pressure broadened spectral lines, it is possible to retrieve the vertical distribution of the observed species. The vertical range is dependent on the bandwidth and resolution of the spectrometers. The radiometers from the IAP perform ground-based and airborne measurements. For the spectral analysis, they used acoustooptical spectrometers (AOS) or low-resolution filterbanks until recently. Unfortunately, AOS proved to be deficient under conditions encountered in an aircraft. For this reason, the approach of using novel real-time digital fast Fourier transform (FFT) spectrometers of bandwidth 1 GHz was chosen. In this paper, we present measurements of atmospheric trace constituents using digital FFT spectrometers and compare the results with measurements from the AOS and filterbank. The FFT spectrometer is superior in resolution and system stability as well as in the linearity and stability of the frequency axis. An important point is also the lower costs per bandwidth and resolution. The measured intensities of emitted radiation from the atmosphere from all spectrometer types were in good agreement. View full abstract»

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  • Experiments and Algorithms to Detect Snow Avalanche Victims Using Airborne Ground-Penetrating Radar

    Page(s): 2240 - 2251
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1202 KB) |  | HTML iconHTML  

    Snow avalanche victims have only a good chance to survive when they are located within a short time. This requires an active beacon for them to wear or a very rapid deployment of a search-and-rescue team with dogs. Customary ground-penetrating radar (GPR) instruments used on the snow surface are not able to reduce fatality numbers because they are slow to search a field. A potential alternative could be an airborne search using radar. An airborne radar search is technologically challenging because a very large data stream has to be processed and visualized in real time, and the interaction of the electromagnetic waves with snow, subsurface, and objects must be understood. We studied a two-step algorithm to locate avalanche victims in real time. The algorithm was validated using realistic test arrangements and conditions using an aerial tramway. The distance dependence of the reflection energy with increased flight heights, the coherence between the use of more antennas and the detectable range, and the reflection images of different avalanche victims were measured. The algorithm detected an object for each investigated case, where the reflection energy of the scans was higher than for the scans of pure snow. Airborne GPR has a large potential to become a rapid search method in dry snow avalanches. However, a fully operational version still requires substantial improvements in hardware and software. View full abstract»

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  • A Waveform Model for Near-Nadir Radar Altimetry Applied to the Cassini Mission to Titan

    Page(s): 2252 - 2261
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (328 KB) |  | HTML iconHTML  

    The radar altimeter of the Cassini mission to Titan operates in a transition region between pulse- and beam-limited conditions. Due to the specific observation geometry, low values of mispointing angle have been found to significantly affect altimeter impulse response (IR). This involves a nonconventional formulation of the system response which is the main goal of this paper. An analytical model of the average return power waveform, valid for near-nadir altimetry measurements, has been developed in order to cope with the particular operating conditions of Cassini mission. The model used to approximate the altimeter waveform is based on the same general assumptions of the classical Brown's model (1977) but exploits a flat surface response approximation by Prony's methods. Both theoretical considerations and simulated data have been taken into account to support the accuracy of the proposed model. To infer the main geophysical parameters describing surface topography from altimetry data, a parametric estimation procedure has been used. The maximum likelihood estimator procedure has been chosen since, in principle, it can assure optimal performance as a consequence of the analytical model we used to describe the system IR. Performances of the implemented method have been numerically evaluated through simulation of data received by CASSINI in high-resolution altimeter mode. View full abstract»

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  • Alternatives to Target Entropy and Alpha Angle in SAR Polarimetry

    Page(s): 2262 - 2274
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1458 KB) |  | HTML iconHTML  

    The purpose of this paper is to discuss two polarimetric parameters which are widely used in synthetic aperture radar (SAR) polarimetry, namely, target entropy and alpha angle. We propose alternative parameters based on our analysis on how they are connected to covariance matrix similarity invariants and how they can be physically interpreted in optical polarimetry. The proposed alternatives can be computed by a fairly simple algorithm and even by the use of software without complex mathematics abilities. As an example, a NASA/Jet Propulsion Laboratory Airborne SAR L-band image of the San Francisco Bay is used to compare the proposed parameter schemes with the original entropy and alpha. A coherent rationale for these alternative parameters is formulated in order to provide insight to polarimetric parameter interpretation. View full abstract»

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  • Focusing Spaceborne/Airborne Hybrid Bistatic SAR Data Using Wavenumber-Domain Algorithm

    Page(s): 2275 - 2283
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (613 KB) |  | HTML iconHTML  

    This paper focuses on the bistatic synthetic aperture radar (SAR) data processing in a spaceborne/airborne hybrid bistatic configuration. Due to the extreme differences in platform velocities and slant ranges, the airborne system operates in the inverse sliding-spotlight mode, while the spaceborne system works in the sliding-spotlight mode to achieve a tradeoff between azimuth scene size and azimuth resolution. In this extreme bistatic configuration, our original bistatic formula shows a limitation of accurately describing the bistatic point-target reference spectrum, owing to the assumption of equal contributions of transmitter and receiver to the total Doppler spectrum. We extend our previous formula using the weighting operation where the weighting factor is the ratio of the azimuth time-bandwidth product (TBP) of the platform to the total azimuth TBP. In this paper, the bistatic-deformation and azimuth-dependent range-cell-migration terms were removed with phase multiplications performed blockwise in range-azimuth subsections. The remaining quasi-monostatic term shows the characteristic of the conventional monostatic SAR besides an additional azimuth-scaling term. For the monostatic characteristic, any precision monostatic SAR processing algorithms can handle. In this paper, we prefer the wavenumber-domain algorithm (also known as Omega-K), since it can accurately correct the range dependence of the range-azimuth coupling, as well as the azimuth-frequency dependence. For the azimuth-scaling term, an inverse scaled Fourier transformation is performed to correct it. Finally, a hybrid spaceborne/airborne simulation experiment is conducted to validate the proposed processing procedure. View full abstract»

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  • Detection of Single Scatterers in Multidimensional SAR Imaging

    Page(s): 2284 - 2297
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1963 KB) |  | HTML iconHTML  

    Multidimensional synthetic aperture radar (SAR) imaging is a technique based on coherent SAR data combination for space (full 3-D) and space deformation-velocity (4-D) analysis. It is an extension of the concepts of SAR interferometry and differential interferometry SAR and offers new options for the analysis and monitoring of ground scenes. In this paper, we consider the problem of detecting single scatterers for localization and monitoring issues. To this end, we resort to a constant false alarm rate (CFAR) detection scheme which can be synthesized according to three different design criteria: generalized likelihood ratio test, Rao test, and Wald test. At the analysis stage, the performance of the aforementioned detector is compared to that of a previously proposed CFAR scheme, based on the multi-interferogram complex coherence and widely used in persistent scatterer interferometry. The analysis is conducted both on simulated and on real SAR data, acquired by ERS-1/2 satellites. Finally, Cramer-Rao lower bounds for the estimation of the scatterer elevation and velocity are provided. View full abstract»

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  • Predicting Topographic and Bathymetric Measurement Performance for Low-SNR Airborne Lidar

    Page(s): 2298 - 2315
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (815 KB) |  | HTML iconHTML  

    Government and commercial airborne light detection and ranging (lidar) systems have enabled extensive measurements of the Earth's surface and land cover over the past decade. There is much interest, however, in employing smaller lidar systems that require less power to enable sensing from small unmanned aerial vehicles or satellites. Technological advances in the performance of small microlasers and photodetector sensitivity have recently enabled the development of experimental airborne lidar systems with low signal-to-noise ratios (LSNRs). Recent government and academic prototypes have indicated that LSNR airborne lidars could significantly increase the fidelity of terrain reconstruction over what is possible with existing conventional lidars. Thus, there is a need to build up a modeling capability for such systems in order to aid in future system and mission design. A numerical sensor simulator has been developed to model the expected returns from LSNR microlaser altimeter systems and predict their performance. Both optical and signal processing system components are considered, along with other factors, including atmospheric effects and surface conditions. Topographic (solid Earth) and bathymetric (littoral zone) measurement scenarios are considered. The analysis of topographic simulation data focuses on the effect of solar noise on SNR and elevation accuracy while bathymetric performance is evaluated with regard to water depth and scan angle for different water clarities. The mission conditions chiefly responsible for limiting the performance of LSNR lidar are discussed in detail, along with suggestions for further algorithm development and system performance evaluation. View full abstract»

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  • A Dynamic Lunar Spectral Irradiance Data Set for NPOESS/VIIRS Day/Night Band Nighttime Environmental Applications

    Page(s): 2316 - 2329
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (994 KB) |  | HTML iconHTML  

    In anticipation of the first fully calibrated nighttime low-light measurements from the National Polar-orbiting Operational Environmental Satellite System's Visible/Infrared Imager/Radiometer Suite (VIIRS) Day/Night Band (DNB), a simple model has been developed for quantifying the highly variable top-of-atmosphere spectral irradiance (in watts per square meter per micrometer) from Earth's only natural satellite-the Moon. Based on the state-of-the-art in solar source observations, lunar spectral albedo data, and an account for the time-varying Sun/Earth/Moon geometry and lunar phase, the model produces 1-nm resolution irradiance spectra over the interval [0.3, 1.2 mum] for a given date and time. Convolving the spectra with the sensor response function of the VIIRS/DNB allows for the conversion from measurements of upwelling radiance to equivalent lunar reflectance [i.e., 0%-100%]-enabling quantitative nighttime multispectral applications that have heretofore been restricted to the daytime hours for lack of visible reflectance information. In the interest of advancing research in nighttime environments, we present here the development and validation of a lunar spectral irradiance database, supply auxiliary data and tools necessary for computing temporally dependent values, and discuss some of the environmental applications enabled by this utility. View full abstract»

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  • Measurement of Reflectance Properties of Asphalt Surfaces and Their Usability as Reference Targets for Aerial Photos

    Page(s): 2330 - 2339
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1455 KB) |  | HTML iconHTML  

    Reference targets with known reflectance properties are needed in remote-sensing in-flight calibration. The spectral and directional reflection properties of nine asphalt surfaces and concrete, and sand were measured. Corresponding polarization properties were also measured for five asphalts and for both sand and concrete. Measurements were obtained using the Finnish Geodetic Institute Field Goniospectrometer. The newly constructed smooth asphalt surfaces had lowest reflectances, and they produced strong forward scatter. The aged and deteriorated surfaces produced more isotropic scatter. The overall reflectance of the aged surfaces was higher than that of the newly constructed surfaces, and they were darkest when viewed close to nadir. Near nadir reflectance of all asphalt surfaces had low angular dependence. Light reflected from the newly constructed asphalt surfaces was found to have a large polarization ratio in the forward direction, as the aged asphalt surfaces were found to be less polarizing. All measured asphalt surfaces were spectrally flat, without dominating features. The measurements showed clearly that asphalt surfaces cannot be used as stable reflection targets without additional knowledge of the asphalt surface. Asphalt can serve as medium-accuracy white balancing media, but more quantitative use for calibration purposes requires the reflection properties to be either individually measured at each location or the properties of the asphalt to be known. The latter is a possible practical element in digital aerial image calibration, but it requires further studies. View full abstract»

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  • Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission

    Page(s): 2340 - 2351
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1239 KB) |  | HTML iconHTML  

    The simulation of remote-sensing images is a useful tool for a variety of tasks, such as the definition of future Earth Observation systems, the optimization of instrument specifications, and the development and validation of data processing algorithms. A scene simulator for optical hyperspectral and multispectral data has been implemented in the frame of the Environmental Mapping and Analysis Program (EnMAP) mission. EnMAP is a German-built hyperspectral space sensor scheduled for launch in 2012. EnMAP will measure in the 420-2450-nm spectral range at a varying spectral sampling of 6.5-10 nm. Images will cover 30 times 30 km areas at an approximate ground sampling distance of 30 m. The EnMAP scene simulator presented in this paper is able to generate realistic EnMAP-like data in an automatic way under a set of user-driven instrumental and scene parameters. Radiance and digital numbers data are generated by five sequential processing modules which are able to produce data over a range of natural environments, acquisition and illumination geometries, cloud covers, and instrument configurations. The latter include the simulation of data nonuniformity in the spatial and spectral domains, spatially coherent and noncoherent instrumental noise, and instrument's modulation transfer function. Realistic surface patterns for the simulated data are provided by existing remote-sensing data in different environments, from dry geological sites to green vegetation areas. A flexible radiative transfer simulation scheme enables the generation of different illumination, observation, and atmospheric conditions. The methodology applied to the complete scene simulation and some sample results are presented and analyzed in this paper. View full abstract»

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Aims & Scope

 

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.

 

Full Aims & Scope

Meet Our Editors

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