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Geoscience and Remote Sensing, IEEE Transactions on

Issue 9 • Date Sept. 2010

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Displaying Results 1 - 25 of 30
  • [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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  • Table of contents

    Page(s): 3329 - 3330
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  • Assessment of AMSR-E Antarctic Winter Sea-Ice Concentrations Using Aqua MODIS

    Page(s): 3331 - 3339
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1106 KB) |  | HTML iconHTML  

    An assessment of the standard Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sea-ice concentrations for the Antarctic winter is made from a comparison of nearly 40 000 AMSR-E sea-ice concentration values with geolocated sea-ice concentrations derived from ten Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) scenes acquired on October 1st and 2nd of 2005 and 2006. The standard AMSR-E sea-ice concentration products are produced using the National Aeronautics and Space Administration Team 2 sea-ice algorithm. The ten MODIS scenes cover portions of almost all the sea-ice regions surrounding the Antarctic continent. The AMSR-E averaged ice concentration biases relative to MODIS (AMSR-E minus MODIS) ranged from less than -0.5% to - 18%, and the corresponding averaged root-mean-square (rms) errors ranged from 2% to 24%. One scene [October 1, 2006 (0550 UT)] had both the largest bias (-18%) and rms error (24%), whereas the other nine scenes had an average bias of - 1.5% and an average rms error of 4.9%. The biases and rms errors are correlated with the fractions of new ice and open water. This is consistent with the findings that the largest errors in ice concentration derived from the AMSR-E occur in the marginal ice zone (MIZ) and along the ice edge and are likely caused by sea-ice flooding in the MIZ and new-ice production at the ice edge. View full abstract»

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  • Estimation of the Backscatter Vertical Profile of a Pine Forest Using Single Baseline P-Band (Pol-)InSAR Data

    Page(s): 3340 - 3348
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    The vertical backscatter profile of a pine forest constituted by stands of different height is inverted from a single baseline P-band Pol-InSAR data in order to identify scatterers in the canopy. The proposed approach uses the Gaussian vertical backscatter profile model, which associates an interferometric coherence expression to a vertical scatterers' distribution characterized by relative standard deviation and elevation. The methodology, which uses in situ measurements of forest height and unbiased ground level estimation, is applied to HV and VV channels, providing accuracy given sufficiently low ground-to-canopy power ratios. Inverted backscatter profiles show maximum power converging toward the basis of the tree crown on highest forests, where the largest branches are located, indicating the high sensitivity of P-band measurements to the forest structure and to the vertical biomass distribution. Over lower stands with larger tree densities, the power peak is located in the upper part of the canopy, which can be explained by a stronger attenuation in the canopy. View full abstract»

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  • A General Characterization for Polarimetric Scattering From Vegetation Canopies

    Page(s): 3349 - 3357
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    Current polarimetric model-based decomposition techniques are limited to specific types of vegetation because of their assumptions about the volume scattering component. In this paper, we propose a generalized probability density function based on the nth power of a cosine-squared function. This distribution is completely characterized by two parameters; a mean orientation angle and the power of the cosine-squared function. We show that the underlying randomness of the distribution is only a function of the power of the cosine-squared function. We then derive the average covariance matrix for various different elementary scatterers showing that the result has a very simple analytical form suitable for use in model-based decomposition schemes. View full abstract»

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  • A Field Platform for Continuous Measurement of Canopy Fluorescence

    Page(s): 3358 - 3368
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (934 KB) |  | HTML iconHTML  

    This paper presents a field platform for continuous measurement of fluorescence at the canopy level. It consists of a 21-m-high crane equipped for fluorescence measurements. The crane is installed in the middle of the fields dedicated to agricultural research. Thanks to a jib of 24 m and a railway of 100 m distance, fluorescence measurements can be performed at nadir viewing over various field crops. The platform is dedicated to the development and test of future passive or active airborne and space-borne vegetation sensors. A new fully automatic instrument, called TriFLEX, has been installed at the end of the jib. TriFLEX is designed for passive measurement of fluorescence in the oxygen A and B absorption bands. It is based on three spectrometers and allows for continuous measurements with a repetition rate of about 1 Hz. The data products are the radiances of the target, the fluorescence flux at 687 and 760 nm, and several vegetation indexes, including the photochemical reflectance index and the normalized difference vegetation index. A new algorithm for fluorescence retrieval from spectral bands measurement is described. It improves upon the well-known Fraunhofer line discriminator method applied to passive fluorescence measurement by taking into account the spectral shape of fluorescence and the reflectance of vegetation. A measurement campaign of 38 days has been carried out in summer 2008 over a sorghum field. The evolution of the signals showed that the crop was suffering from stress due to lack of water. After several rainy days, a reversion of the water stress was observed. View full abstract»

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  • Simulating X-Band Interferometric Height in a Spruce Forest From Airborne Laser Scanning

    Page(s): 3369 - 3378
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (369 KB) |  | HTML iconHTML  

    The aim of this study is to use airborne laser scanning (ALS) data to simulate synthetic aperture radar interferometry (InSAR) elevation data [digital elevation model (DEM)] from the spatial distribution of scatterers. A Shuttle Radar Topography Mission X-band DEM data set and an ALS data set from a spruce-dominated forest area are used. A 3-D grid of voxels is made from the spatial distribution of ALS first echoes. The slant angle penetration rate of the SAR microwaves (PSAR) is simulated to be a function of the vertical ALS penetration rate (PALS), i.e., PSAR = PALS4. The InSAR DEM and heights above the ground are fairly well reproduced by the simulator. A total least squares regression model between the simulated and measured InSAR DEMs has an R2 value of 0.99 and a slope of 1 : 1. By subtracting the ALS-based terrain heights (digital terrain model), we obtained InSAR heights, which were reproduced with an R2 value of 0.78, a slope of 0.96, and a root-mean-square error of 2.3 m. With the simulator, it was demonstrated how a disturbance event would affect the InSAR height. Unfortunately, the relationship was curvilinear and concave, which means that the method is not very sensitive to weak disturbances. This might be partly overcome by using a more vertical incidence angle of the SAR microwaves. The simulator might be used for validation or ground truthing of the InSAR data, as well as gaining understanding of how vegetation changes affect the InSAR data. View full abstract»

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  • Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors

    Page(s): 3379 - 3390
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    This paper presents an evaluation of airborne sensors for use in vegetation management in power-line corridors. Three integral stages in the management process are addressed, including the detection of trees, relative positioning with respect to the nearest power line, and vegetation height estimation. Image data, including multispectral and high resolution, are analyzed along with LiDAR data captured from fixed-wing aircraft. Ground truth data are then used to establish the accuracy and reliability of each sensor, thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a pulse-coupled neural network and morphologic reconstruction applied to multispectral imagery. Through testing, it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved root-mean-square-error (rmse) values of 1.4 and 2.1 m for cross-track distance and along-track position, respectively, while direct georeferencing achieved rmse of 3.1 m in both instances. The estimation of pole and tree heights measured with LiDAR had rmse values of 0.4 and 0.9 m, respectively, while stereo matching achieved 1.5 and 2.9 m. Overall, a small number of poles were missed with detection rates of 98% and 95% for LiDAR and stereo matching. View full abstract»

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  • A New Vector Waveform Inversion Algorithm for Simultaneous Updating of Conductivity and Permittivity Parameters From Combination Crosshole/Borehole-to-Surface GPR Data

    Page(s): 3391 - 3407
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1883 KB) |  | HTML iconHTML  

    We have developed a new full-waveform groundpenetrating radar (GPR) multicomponent inversion scheme for imaging the shallow subsurface using arbitrary recording configurations. It yields significantly higher resolution images than conventional tomographic techniques based on first-arrival times and pulse amplitudes. The inversion is formulated as a nonlinear least squares problem in which the misfit between observed and modeled data is minimized. The full-waveform modeling is implemented by means of a finite-difference time-domain solution of Maxwell's equations. We derive here an iterative gradient method in which the steepest descent direction, used to update iteratively the permittivity and conductivity distributions in an optimal way, is found by cross-correlating the forward vector wavefield and the backward-propagated vectorial residual wavefield. The formulation of the solution is given in a very general, albeit compact and elegant, fashion. Each iteration step of our inversion scheme requires several calculations of propagating wavefields. Novel features of the scheme compared to previous full-waveform GPR inversions are as follows: 1) The permittivity and conductivity distributions are updated simultaneously (rather than consecutively) at each iterative step using improved gradient and step length formulations; 2) the scheme is able to exploit the full vector wavefield; and 3) various data sets/survey types (e.g., crosshole and borehole-to-surface) can be individually or jointly inverted. Several synthetic examples involving both homogeneous and layered stochastic background models with embedded anomalous inclusions demonstrate the superiority of the new scheme over previous approaches. View full abstract»

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  • Discrete Almost-Symmetric Wave Packets and Multiscale Geometrical Representation of (Seismic) Waves

    Page(s): 3408 - 3423
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    We discuss a multiscale geometrical representation of (seismic) waves through a decomposition into wave packets. Wave packets can be thought of as certain localized “fat” plane waves. Here, we construct discrete almost-symmetric 3-D wave packets by using the unequally spaced fast Fourier transform. The resulting discrete transform is unitary, implying that the reconstruction operator is simply the adjoint of the decomposition operator. Another relevant aspect of the discretization scheme is the appearance of parameters that control the tiling of the phase space that corresponds with the dyadic parabolic decomposition, preserving the relative parabolic scaling while adapting to the physical problem at hand. We consider applications in exploration and global seismology, in particular for higher dimensional data regularization, seismic map migration, denoising, directional regularity analysis, and feature extraction. View full abstract»

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  • Precise Subpixel Disparity Measurement From Very Narrow Baseline Stereo

    Page(s): 3424 - 3433
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1749 KB) |  | HTML iconHTML  

    To obtain depth-from-stereo imagery, it is traditionally required that the baseline separation between images (or the base-to-height ratio) be very large in order to ensure the largest image disparity range for effective measurement. Typically, a B/H ratio in the range of 0.6-1 is preferred. As a consequence, most existing stereo-matching algorithms are designed to measure disparities reliably with only integer-pixel precision. However, wide baselines may increase the possibility of occlusion occurring between highly contrasting relief, imposing a serious problem to digital elevation model (DEM) generation in urban and highly dissected mountainous areas. A narrow-baseline stereo configuration can alleviate the problem significantly but requires very precise measurements of disparity at subpixel levels. In this paper, we demonstrate a stereo-matching algorithm, based upon the robust phase correlation method, that is capable of directly measuring disparities up to 1/50th pixel accuracy and precision. The algorithm enables complete and dense surface shape information to be retrieved from images with unconventionally low B/H ratios (e.g., less than 0.01), potentially allowing DEM generation from images that would otherwise not be deemed suitable for the purpose. View full abstract»

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  • Spatial Purity Based Endmember Extraction for Spectral Mixture Analysis

    Page(s): 3434 - 3445
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (803 KB) |  | HTML iconHTML  

    Spectral mixture analysis (SMA) has been widely utilized to address the mixed-pixel problem in the quantitative analysis of hyperspectral remote sensing images, in which endmember extraction (EE) plays an extremely important role. In this paper, a novel algorithm is proposed to integrate both spectral similarity and spatial context for EE. The spatial context is exploited from two aspects. At first, initial endmember candidates are identified by determining the spatial purity (SP) of pixels in their spatial neighborhoods (SNs). Several SP measurements are investigated at both intensity level and feature level. In order to alleviate local spectra variability, the average of the pixels in pure SNs are voted as endmember candidates. Then, the spatial connectivity is utilized to merge spatially related endmember candidates by finding connection paths in a graph so that the number of endmember candidates is further reduced, which results in computational efficiency and better performance in SMA by alleviating global spectral variability. Experimental results on both synthetic and real hyperspectral images demonstrate that the proposed SP based EE (SPEE) algorithm outperforms the other popular EE algorithms. It is also observed that feature-level SP measurements are more distinguishable than intensity-level SP measurements to discriminate pure SNs from mixed SNs. View full abstract»

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  • A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features

    Page(s): 3446 - 3456
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (751 KB) |  | HTML iconHTML  

    Ship detection from remote sensing imagery is very important, with a wide array of applications in areas such as fishery management, vessel traffic services, and naval warfare. This paper focuses on the issue of ship detection from spaceborne optical images (SDSOI). Although advantages of synthetic-aperture radar (SAR) result in that most of current ship detection approaches are based on SAR images, disadvantages of SAR still exist, such as the limited number of SAR sensors, the relatively long revisit cycle, and the relatively lower resolution. With the increasing number of and the resulting improvement in continuous coverage of the optical sensors, SDSOI can partly overcome the shortcomings of SAR-based approaches and should be investigated to help satisfy the requirements of real-time ship monitoring. In SDSOI, several factors such as clouds, ocean waves, and small islands affect the performance of ship detection. This paper proposes a novel hierarchical complete and operational SDSOI approach based on shape and texture features, which is considered a sequential coarse-to-fine elimination process of false alarms. First, simple shape analysis is adopted to eliminate evident false candidates generated by image segmentation with global and local information and to extract ship candidates with missing alarms as low as possible. Second, a novel semisupervised hierarchical classification approach based on various features is presented to distinguish between ships and nonships to remove most false alarms. Besides a complete and operational SDSOI approach, the other contributions of our approach include the following three aspects: 1) it classifies ship candidates by using their class probability distributions rather than the direct extracted features; 2) the relevant classes are automatically built by the samples' appearances and their feature attribute in a semisupervised mode; and 3) besides commonly used shape and texture features, a new texture operator, i.e., loca- - l multiple patterns, is introduced to enhance the representation ability of the feature set in feature extraction. Experimental results of SDSOI on a large image set captured by optical sensors from multiple satellites show that our approach is effective in distinguishing between ships and nonships, and obtains a satisfactory ship detection performance. View full abstract»

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  • Hyperspectral Region Classification Using a Three-Dimensional Gabor Filterbank

    Page(s): 3457 - 3464
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    A 3-D spectral/spatial discrete Fourier transform can be used to represent a hyperspectral image region using a dense sampling in the frequency domain. In many cases, a more compact frequency-domain representation that preserves the 3-D structure of the data can be exploited. For this purpose, we have developed a new model for spectral/spatial information based on 3-D Gabor filters. These filters capture specific orientation, scale, and wavelength-dependent properties of hyperspectral image data and provide an efficient means of sampling a 3-D frequency-domain representation. Since 3-D Gabor filters allow for a large number of spectral/spatial features to be used to represent an image region, the performance and efficiency of algorithms that use this representation can be further improved if methods are available to reduce the size of the model. Thus, we have derived methods for selecting features that emphasize the most significant spectral/spatial differences between the various classes in a scene. We demonstrate the performance of the 3-D Gabor features for the classification of regions in Airborne Visible/Infrared Imaging Spectrometer hyperspectral data. The new features are compared against pure spectral features and multiband generalizations of gray-level co-occurrence matrix features. View full abstract»

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  • Applications of Grid Pattern Matching to the Detection of Buried Landmines

    Page(s): 3465 - 3470
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (674 KB) |  | HTML iconHTML  

    Whereas overhead infrared imagery shows promise for detecting buried landmines, detection algorithms must deal with the daunting challenge of distinguishing between landmines and clutter objects which frequently possess similar spatial and spectral characteristics to landmines. However, groups of clutter features are rarely related spatially in the same way that groups of mines are related. For this reason, the recognition of minefield patterns in overhead landmine imagery can be useful to the detection of mines in minefields. In this paper, we present a simple method for detecting grid patterns in imagery, discuss means by which the method may be extended to a more general category of patterns, provide a method for the automated prediction of the locations of undetected mines based upon the observed pattern, and finally we discuss applications. Examples are provided using longwave infrared hyperspectral imagery. View full abstract»

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  • Feature-Driven Multilayer Visualization for Remotely Sensed Hyperspectral Imagery

    Page(s): 3471 - 3481
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1581 KB) |  | HTML iconHTML  

    Displaying the abundant information contained in a remotely sensed hyperspectral image is a challenging problem. Currently, no approach can satisfactorily render the desired information at arbitrary levels of detail. In this paper, we present a feature-driven multilayer visualization technique that automatically chooses data visualization techniques based on the spatial distribution and importance of the endmembers. It can simultaneously visualize the overall material distribution, subpixel level details, and target pixels and materials. By incorporating interactive tools, different levels of detail can be presented per users' request. This scheme employs five layers from the bottom to the top: the background layer, data-driven spot layer, pie-chart layer, oriented sliver layer, and anomaly layer. The background layer provides the basic tone of the display; the data-driven spot layer manifests the overall material distribution in an image scene; the pie-chart layer presents the precise abundances of endmember materials in each pixel; the oriented sliver layer emphasizes the distribution of important anomalous materials; and the anomaly layer highlights anomaly pixels (i.e., potential targets). Displays of the airborne AVIRIS data and spaceborne Hyperion data demonstrate that the proposed multilayer visualization scheme can efficiently display more information globally and locally. View full abstract»

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  • Operational Performance of an Automatic Preliminary Spectral Rule-Based Decision-Tree Classifier of Spaceborne Very High Resolution Optical Images

    Page(s): 3482 - 3502
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1588 KB) |  | HTML iconHTML  

    In the last 20 years, the number of spaceborne very high resolution (VHR) optical imaging sensors and the use of satellite VHR optical images have continued to increase both in terms of quantity and quality of data. This has driven the need for automating quantitative analysis of spaceborne VHR optical imagery. Unfortunately, existing remote sensing image understanding systems (RS-IUSs) score poorly in operational contexts. In recent years, to overcome operational drawbacks of existing RS-IUSs, an original two-stage stratified hierarchical RS-IUS architecture has been proposed by Shackelford and Davis. More recently, an operational automatic pixel-based near-real-time four-band IKONOS-like spectral rule-based decision-tree classifier (ISRC) has been downscaled from an original seven-band Landsat-like SRC (LSRC). The following is true for ISRC: (1) It is suitable for mapping spaceborne VHR optical imagery radiometrically calibrated into top-of-atmosphere or surface reflectance values, and (2) it is eligible for use as the pixel-based preliminary classification first stage of a Shackelford and Davis two-stage stratified hierarchical RS-IUS architecture. Given the ISRC “full degree” of automation, which cannot be surpassed, and ISRC computation time, which is near real time, this paper provides a quantitative assessment of ISRC accuracy and robustness to changes in the input data set consisting of 14 multisource spaceborne images of agricultural landscapes selected across the European Union. The collected experimental results show that, first, in a dichotomous vegetation/nonvegetation classification of four synthesized VHR images at regional scale, ISRC, in comparison with LSRC, provides a vegetation detection accuracy ranging from 76% to 97%, rising to about 99% if pixels featuring a low leaf area index are not considered in the comparison. Second, in the generation of a binary vegetation mask from ten panchromatic-sharpened QuickBird-2 and IKONOS-2 im- - ages, the operational performance measurement of ISRC is superior to that of an ordinary normalized difference vegetation index thresholding technique. Finally, the second-stage automatic stratified texture-based separation of low-texture annual cropland or herbaceous range land (land cover class AC/HR) from high-texture forest or woodland (land cover class F/W) is performed in the discrete, finite, and symbolic ISRC map domain in place of the ordinary continuous varying, subsymbolic, and multichannel texture feature domain. To conclude, this paper demonstrates that the automatic ISRC is eligible for use in operational VHR satellite-based measurement systems such as those envisaged under the ongoing Global Earth Observation System of Systems (GEOSS) and Global Monitoring for the Environment and Security (GMES) international programs. View full abstract»

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  • Automatic Fuzzy Clustering Using Modified Differential Evolution for Image Classification

    Page(s): 3503 - 3510
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1179 KB) |  | HTML iconHTML  

    The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types, some of which cover significantly large areas while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes is a challenging task. In this paper, a new real-coded modified differential evolution based automatic fuzzy clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Here, the assignment of points to different clusters is done based on a Xie-Beni index where the Euclidean distance is taken into consideration. The effectiveness of the proposed technique is first demonstrated for two numeric remote sensing data described in terms of feature vectors and then in identifying different landcover regions in remote sensing imagery. The superiority of the new method is demonstrated by comparing it with other existing techniques like automatic clustering using improved differential evolution, classical differential evolution based automatic fuzzy clustering, variable length genetic algorithm based fuzzy clustering, and well known fuzzy C-means algorithm both qualitatively and quantitatively. View full abstract»

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  • Using Aerial Imagery and GIS in Automated Building Footprint Extraction and Shape Recognition for Earthquake Risk Assessment of Urban Inventories

    Page(s): 3511 - 3520
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1293 KB) |  | HTML iconHTML  

    Earthquakes cause massive loss of property and lives, and mitigating their potential effects requires accurate modeling and simulation of their impacts. Earthquake building damage modeling and risk assessment applications require accurate accounts of inventories at risk and their attributes such as structure type, usage, size, number of stories, shape, year built, value, etc. This paper describes the development of algorithms for automatically extracting and recognizing 2-D building shape information using integrated aerial imagery processing and Geographic Information Systems data. We use vector parcel geometries and their attributes to simplify the building extraction task by limiting the processing geography. Extraction is significantly improved by innovatively weighting the histograms. Extracted buildings are cleaned, simplified, and run through 2-D shape recognition routines that classify the footprint. We discuss reasons for successes and failures in both extraction and recognition. View full abstract»

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  • An End-to-End Error Model for Classification Methods Based on Temporal Change or Polarization Ratio of SAR Intensities

    Page(s): 3521 - 3538
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1141 KB) |  | HTML iconHTML  

    This paper aims at defining the expression of the probability of error of classification methods using a synthetic aperture radar (SAR) intensity ratio as a classification feature. The two SAR intensities involved in this ratio can be measurements from different dates, polarizations, or, also possibly, frequency bands. Previous works provided a baseline expression of the probability of error addressing the two-class problem with equal a priori class probabilities and no calibration error. This study brings up a novel expression of the error, providing the possibility to assess the effect of class probabilities and calibration errors. An extended expression is described for the n -class problem. The effect of calibration errors such as channel gain imbalance, radiometric stability, and crosstalk is assessed in the general case. The results indicate that, for the applications under study, channel gain imbalance is usually not a decisive parameter, but radiometric stability is more critical in methods based on the temporal change. Crosstalk has a negligible effect in the case of copolarizations. The impacts of other system parameters, such as ambiguity ratio, time-lapse between repeat-pass orbits, spatial resolution, and number of looks, are illustrated through a set of assumptions on the backscattering values of the considered classes. The model is validated by comparing some of its outputs to experimental results calculated from the application of rice fields mapping methods on real data. This error model constitutes a tool for the design of future SAR missions and for the development of robust classification methods using existing SAR instruments. View full abstract»

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  • HFSW Radar Model: Simulation and Measurement

    Page(s): 3539 - 3549
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1769 KB) |  | HTML iconHTML  

    High-frequency surface-wave (HFSW) radars are usually used to remotely measure oceanographic parameters. These systems can also potentially detect targets beyond the conventional microwave radar coverage. In this paper, the backscattered Doppler spectrum made up of the sea clutter, ship echoes, and the background noise has been modeled. Taking into account the propagation and the signal-processing effects, a range-Doppler image has been generated. This model can be used for different purposes like the (theoretical) evaluation of detection performance. This paper gives an overview of the theoretical elements for modeling the backscatter signal. The processing effects on the range-Doppler image and the time-evolving target signature are also introduced. Some of the simulated elements and the obtained range-Doppler images are compared with real data. Finally, from this model, the detection capabilities of HFSW radars are evaluated. View full abstract»

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  • Mapping of Sand Layer Thickness in Deserts Using SAR Interferometry

    Page(s): 3550 - 3559
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1623 KB) |  | HTML iconHTML  

    This paper presents an interferometric synthetic aperture radar (InSAR) system to map the bedrock topography underneath the sand in deserts and arid areas. This is anticipated to greatly increase the efficiency of oil field and ground water exploration as well as environmental and archaeological studies. The proposed system consists of two InSAR subsystems, one operating at Ka-band to map the sand topography and the other operates in the VHF band to map the subsurface topography. The different issues associated with InSAR processing for subsurface mapping are discussed. It is shown that conventional InSAR processing produces unacceptable error in height estimation since it does not account for the refraction and the different propagation velocity in the sand. Thus, a new inversion algorithm is developed which can be used to accurately estimate the bedrock topography for arbitrary sand and bedrock geometries. A sensitivity analysis is then presented to show the effect of the different systematic and random errors. The inversion algorithm is verified experimentally for flat sand case using a scaled model that was implemented in the lab. View full abstract»

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  • Modeling and Analysis of Polarimetric Synthetic Aperture Interferometric Radiometers Using Noise Waves

    Page(s): 3560 - 3570
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1393 KB) |  | HTML iconHTML  

    Polarimetric synthetic aperture interferometric radiometers are analyzed, including the polarization leakage and other front-end non-idealities that can be measured and represented with S-parameters. The analysis utilizes the noise wave concept in order to account for the amplitude and phase properties of the front-end. The method is applied to a model of an entire synthetic aperture interferometric radiometer. The simulations can be used to examine and retrieve requirements for the system parameters. The feasibility of the method is demonstrated and examples of end-to-end simulation results are also given in this paper. View full abstract»

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  • Mutual Enhancement of Weak Laser Pulses for Point Cloud Enrichment Based on Full-Waveform Analysis

    Page(s): 3571 - 3579
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1656 KB) |  | HTML iconHTML  

    In this paper, we present a novel method for weak laser pulse detection by full-waveform analysis. Pulse detection is a fundamental step of processing data of pulsed laser systems for extracting features of the illuminated object. Weak laser pulses below the threshold are discarded by classical methods. For full-waveform laser scanners, the entire recording of a scene can be interpreted as a discrete waveform cuboid I[x y t]-, where the measured amplitude at each time t and each beam direction [x y] is stored. The potential information hiding in the waveform cuboid could be utilized to improve the analysis result of conventional system. The neighborhood relation given by co-planarity constraint in waveform data is analyzed. Waveform stacking technique is introduced to improve the signalto-noise ratio (SNR) of objects with poor surface response in view of mutual information enhancement. Hypotheses for planar surface of different slopes are generated and verified. Each pulse signal is assessed with respect to accepted hypotheses by a contribution measure to the local geometry. Pulse signals are finally classified according to the likelihood value by automatically thresholding. The presented method was applied to waveform data of an urban scene and showed very promising results. The pulses reflected from objects with poor surface response or partially occluded are redetected, which cannot be predicted by given geometric models based on available points. 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