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Geoscience and Remote Sensing, IEEE Transactions on

Issue 5  Part 2 • Date May 2007

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Displaying Results 1 - 25 of 26
  • [Front cover]

    Publication Year: 2007 , Page(s): C1
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  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Publication Year: 2007 , Page(s): C2
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  • Table of contents

    Publication Year: 2007 , Page(s): 1301 - 1302
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  • Retrieval of Tangent Pressures From EOS–MLS Radiances Using a Neural Network for Use in an Assimilation Scheme

    Publication Year: 2007 , Page(s): 1303 - 1307
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (141 KB) |  | HTML iconHTML  

    Limb sounding instruments provide high vertical resolution data on the temperature and composition of the atmosphere. Their data are, therefore, valuable for assimilating into general circulation models of the atmosphere. Direct assimilation of radiances from limb sounders is more complex in practice than from nadir sounders due to the need to know the tangent pressures of the measurements. This paper discusses the practical implications of tangent pressures in direct radiance assimilation of limb sounding radiances and demonstrates that a neural network can be used to find these tangent pressures for the Earth Observing System Microwave Limb Sounder with a root mean-square error of sigma=50 m, which is comparable with that in traditional retrieval techniques View full abstract»

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  • A Method to Determine the Spatial Resolution Required to Observe Air Quality From Space

    Publication Year: 2007 , Page(s): 1308 - 1314
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (341 KB) |  | HTML iconHTML  

    Satellite observations have the potential to provide an accurate picture of atmospheric chemistry and air quality on a variety of spatial and temporal scales. A key consideration in the design of new instruments is the spatial resolution required to effectively monitor air quality from space. In this paper, variograms have been used to address this issue by calculating the horizontal length scales of ozone within the boundary layer and free troposphere using both in situ aircraft data from five different NASA aircraft campaigns and simulations with an air-quality model. For both the observations and the model, the smallest scale features were found in the boundary layer, with a characteristic scale of about 50 km which increased to greater than 150 km above the boundary layer. The length scale changes with altitude. It is shown that similar length scales are derived based on a totally independent approach using constituent lifetimes and typical wind speeds. To date, the spaceborne observations of tropospheric constituents have been from several instruments including TOMS, GOME, MOPITT, TES, and OMI which, in general, have different weighting functions that need to be considered, and none really measures at the surface. A further complication is that most satellite measurements (such as those of OMI and GOME) are of the vertically integrated column. In this paper, the length scales in the column measurements were also of the order of 50 km. To adequately resolve the 50-km features, a horizontal resolution of at least 10 km would be desirable View full abstract»

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  • The Sensitivity of Ice Cloud Optical and Microphysical Passive Satellite Retrievals to Cloud Geometrical Thickness

    Publication Year: 2007 , Page(s): 1315 - 1323
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1054 KB) |  | HTML iconHTML  

    Most satellite-based ice cloud retrieval algorithms rely on precomputed lookup libraries for inferring the ice cloud optical thickness (tau) and effective particle size ( De). However, this retrieval methodology does not account for the case where cloud geometrical thickness may vary by several kilometers. In this paper, we investigate the effect of the ice cloud geometrical thickness on the retrieval of tau and De for algorithms using the Moderate Resolution Imaging Spectroradiometer infrared (IR) bands at 8.5 and 11 mum (or 12 mum) or solar bands at 0.65 and 1.64 mum (or 2.13 mum). We use a rigorous radiative transfer package to simulate the IR brightness temperatures and solar reflectances, assuming that the ice cloud top height is fixed at 12 or 15 km with a variation of cloud geometrical thickness from 0.5 to 5 km. The simulated brightness temperatures and reflectances are then used to investigate the errors of cloud tau and De inferred from the precomputed lookup tables developed with a specific geometrical thickness. It is found that the retrieval errors in tau and De increase with increasing tau for the IR and solar methods. In both cases, cloud tau and De may be underestimated and overestimated, respectively, if the effect of the cloud geometrical thickness is not taken into account. The effect of the cloud geometrical thickness on the retrieval of cloud optical and microphysical properties is much larger for the IR algorithm than for the solar-band-based algorithm. This paper demonstrates that the inclusion of the information about the cloud geometrical thickness may improve the accuracy of the retrieval of the cloud properties on the basis of the precomputed lookup libraries View full abstract»

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  • Suppression of Surface Clutter Interference With Precipitation Measurements by Spaceborne Precipitation Radar

    Publication Year: 2007 , Page(s): 1324 - 1331
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (957 KB) |  | HTML iconHTML  

    The sidelobe surface clutter along the nadir direction severely interferes with the rain echo in the off-nadir angle observations made using a spaceborne Precipitation Radar (PR). A new method to suppress this sidelobe clutter interference is introduced. A characteristic of the 1-D phased array antenna system is that high sidelobes arise along the beam scan plane. The proposed method tilts the antenna beam scan plane from the nadir such that these high sidelobes would not be directed along the nadir direction, along which a specular component of the backscattering radar cross section of the Earth's surface is dominant. The simulation results using the designed parameters of a Ka-band spaceborne PR indicate the validity of this method, which is also quantitatively confirmed using Tropical Rainfall Measuring Mission/PR observation data sets View full abstract»

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  • Improved Physically Based Oceanic Rainfall Algorithm From AMSR-E Data

    Publication Year: 2007 , Page(s): 1332 - 1341
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (761 KB) |  | HTML iconHTML  

    An improved oceanic rainfall algorithm based on a radiative transfer model that reduces many uncertainties of rainfall retrieval was developed using advanced microwave scanning radiometer for Earth Observing System data. Error models were embedded to quantify rainfall uncertainties and to reduce net uncertainties. Six channels (37, 18, and 10 GHz with dual polarization) were utilized in the algorithm. Several developments such as improvement of the freezing-level (FL) retrieval, a weighted average scheme, and enhanced offset correction were implemented in this paper. As a result, rain rate uncertainties were substantially reduced and quantified. To establish error models, drop-size-distribution uncertainty, beam filling error, data calibration uncertainty, and instrument noise were taken into account. These error models were used to compute proper weights of each channel to combine the six rain rates. The algorithm was evaluated with respect to the current operational algorithm (NASA Level 3 rainfall algorithm). It showed more reasonable mean FLs and rain rate estimation than the operational algorithm. Furthermore, pixel-by-pixel-based quantitative error estimates were conducted through the error model View full abstract»

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  • Retrieving TSM Concentration From Multispectral Satellite Data by Multiple Regression and Artificial Neural Networks

    Publication Year: 2007 , Page(s): 1342 - 1350
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (401 KB) |  | HTML iconHTML  

    In this paper, we present different methodologies to estimate the total suspended matter (TSM) concentration in a particular area of the Portuguese coast, from remotely sensed multispectral data, based on single-band models, multiple regression, and artificial neural networks (ANNs). Simulations on different beaches of the study area were performed to determine a relationship between the TSM concentration and the spectral response of the seawater. Based on the in situ measurements, empirical models were established in order to relate the seawater reflectance with the TSM concentration for TERRA/ASTER, SPOT HRVIR, and Landsat/TM. Seven images of these three sensors were calibrated and atmospherically and geometrically corrected. Single-band models, multiple regression, and ANNs were applied to the visible and near-infrared (NIR) bands of these sensors in order to estimate the TSM concentration. Statistical analysis using correlation coefficients and error estimation was employed, aiming to evaluate the most accurate methodology. The chosen methodology was further applied to the seven processed images. The analysis of the root-mean-square errors achieved by both the linear and nonlinear models supports the hypothesis that the relationship between the seawater reflectance and TSM concentration is clearly nonlinear. The ANNs have been shown to be useful in estimating the TSM concentration from reflectance of visible and NIR bands of ASTER, HRVIR, and TM sensors, with better results for ASTER and HRVIR sensors. Maps of TSM concentration were produced for all satellite images processed View full abstract»

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  • A Takagi–Sugeno Fuzzy Rule-Based Model for Soil Moisture Retrieval From SAR Under Soil Roughness Uncertainty

    Publication Year: 2007 , Page(s): 1351 - 1360
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (758 KB) |  | HTML iconHTML  

    Radar remote sensing has shown its potential for retrieving soil moisture from bare soil surfaces. Since the backscattering process is also influenced by soil roughness, the characterization of this roughness is crucial for an accurate soil moisture retrieval. However, several field experiments have shown a large variability of the roughness parameters. Describing these parameters by means of possibility distributions allows to account for their uncertainty. Verhoest et al. introduced a retrieval procedure which calculates from these uncertain roughness parameters the possibility distribution of retrieved soil moisture, from which a soil moisture value and uncertainty upon the retrieval are estimated. The main disadvantage of their technique is the high computational demand, which hampers an operational application. In this paper, a fuzzy modeling approach, which is based on fuzzy rules of the Takagi-Sugeno type, is introduced that accurately simulates the soil moisture and the uncertainty upon its retrieved value as obtained by the possibilistic procedure View full abstract»

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  • Anomaly Detection Based on Wavelet Domain GARCH Random Field Modeling

    Publication Year: 2007 , Page(s): 1361 - 1373
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (713 KB) |  | HTML iconHTML  

    One-dimensional Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is widely used for modeling financial time series. Extending the GARCH model to multiple dimensions yields a novel clutter model which is capable of taking into account important characteristics of a wavelet-based multiscale feature space, namely heavy-tailed distributions and innovations clustering as well as spatial and scale correlations. We show that the multidimensional GARCH model generalizes the casual Gauss Markov random field (GMRF) model, and we develop a multiscale matched subspace detector (MSD) for detecting anomalies in GARCH clutter. Experimental results demonstrate that by using a multiscale MSD under GARCH clutter modeling, rather than GMRF clutter modeling, a reduced false-alarm rate can be achieved without compromising the detection rate View full abstract»

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  • WAVANGLET: An Efficient Supervised Classifier for Hyperspectral Images

    Publication Year: 2007 , Page(s): 1374 - 1385
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (906 KB) |  | HTML iconHTML  

    The new generation of imaging spectrometers onboard planetary missions usually produce hundreds to thousands of images a year, each made up of a thousand to a million spectra with typically several hundred wavelengths. Such huge datasets must be analyzed by efficient yet accurate algorithms. A supervised automatic classification method (hereafter called "wavanglet") is proposed to identify spectral features and classify images in spectrally homogeneous units. It uses four steps: (1) selection of a library composed of reference spectra; (2) application of a Daubechies wavelet transform to referenced spectra and determination of the wavelet subspace that best separates all referenced spectra; and (3) in this selected subspace, determination of the best threshold on the spectral angle to produce detection masks. This application is focused on the Martian polar regions that present three main types of terrains: H2O ice, CO2 ice, and dust. The wavanglet method is implemented to detect these major compounds on near-infrared hyperspectral images acquired by the OMEGA instrument onboard the Mars Express spacecraft. With an overall accuracy of 89%, wavanglet outperforms two generic methods: band ratio (57% accuracy) and spectral feature fitting (83% accuracy). The quantitative detection limits of wavanglet are also evaluated in terms of abundance for H2O and CO2 ices in order to improve the interpretation of the masks View full abstract»

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  • Anisotropic Diffusion in the Hypercube

    Publication Year: 2007 , Page(s): 1386 - 1398
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (720 KB) |  | HTML iconHTML  

    Denoising of hyperspectral images in the domain of imaging spectroscopy by vectorial anisotropic diffusion is addressed. Anisotropic diffusion has shown to be a powerful denoising technique with many applications in several fields of image processing, and in the recent years, some significant advances have been published. However, these had not yet been specifically adapted to hyperspectral imagery. This paper reviews recent advances in anisotropic diffusion for multivalued images, analyzes their application to hyperspectral images, and proposes a new diffusion method which takes advantage of the recent improvements and conforms to the specificities of hyperspectral remote sensing. Some examples are provided using both a noisy image and a clean image with added noise View full abstract»

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  • Sparse Inverse Covariance Estimates for Hyperspectral Image Classification

    Publication Year: 2007 , Page(s): 1399 - 1407
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (329 KB) |  | HTML iconHTML  

    Classification of remotely sensed hyperspectral images calls for a classifier that gracefully handles high-dimensional data, where the amount of samples available for training might be very low relative to the dimension. Even when using simple parametric classifiers such as the Gaussian maximum-likelihood rule, the large number of bands leads to copious amounts of parameters to estimate. Most of these parameters are measures of correlations between features. The covariance structure of a multivariate normal population can be simplified by setting elements of the inverse covariance matrix to zero. Well-known results from time series analysis relates the estimation of the inverse covariance matrix to a sequence of regressions by using the Cholesky decomposition. We observe that discriminant analysis can be performed without inverting the covariance matrix. We propose defining a sparsity pattern on the lower triangular matrix resulting from the Cholesky decomposition, and develop a simple search algorithm for choosing this sparsity. The resulting classifier is used on four different hyperspectral images, and compared with conventional approaches such as support vector machines, with encouraging results View full abstract»

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  • Transform Coding Techniques for Lossy Hyperspectral Data Compression

    Publication Year: 2007 , Page(s): 1408 - 1421
    Cited by:  Papers (57)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (431 KB) |  | HTML iconHTML  

    Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3-D transform to be used for compression. In this paper, we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. First, we select a set of 3-D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen-Loegraveve transform (KLT), and evaluate the coding efficiency of these combinations. Second, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Third, we integrate this, as well as other existing transforms, in the framework of Part 2 of the Joint Photographic Experts Group (JPEG) 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multiclass hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications View full abstract»

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  • A Dempster–Shafer Relaxation Approach to Context Classification

    Publication Year: 2007 , Page(s): 1422 - 1431
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (739 KB) |  | HTML iconHTML  

    A relaxation scheme is proposed in which Dempster-Shafer evidential theory is used to bring the effect of the spatial neighborhood of a pixel into a classification. The benefits include the ability to incorporate uncertainty in the neighborhood information, allowing a stopping criterion to be devised based on increasing the uncertainty contribution of the neighborhood to unity within a prescribed number of iterations. The number of iterations to be used is governed by several factors, including an estimate of how far out in the neighborhood pixels are assumed to be influential. As with standard relaxation labeling, but unlike many other context-sensitive methods, the evidential approach can be initialized from the results of a separate point statistical classification of the image; it is also consistent with multisource analyses based on evidential methods for fusion. A variation of evidential relaxation using considerably simplified neighborhood information is also developed, illustrating that very good results can be obtained without detailed knowledge of the spatial properties of a scene. The new procedures are compared experimentally with standard probabilistic relaxation and the application of Markov random fields View full abstract»

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  • A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis

    Publication Year: 2007 , Page(s): 1432 - 1445
    Cited by:  Papers (87)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1096 KB) |  | HTML iconHTML  

    In this paper, we present a new similarity measure for automatic change detection in multitemporal synthetic aperture radar images. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are estimated by using a cumulant-based series expansion, which approximates probability density functions in the neighborhood of each pixel in the image. The degree of evolution of the local statistics is measured using the Kullback-Leibler divergence. An analytical expression for this detector is given, allowing a simple computation which depends on the four first statistical moments of the pixels inside the analysis window only. The proposed change indicator is compared to the classical mean ratio detector and also to other model-based approaches. Tests on the simulated and real data show that our detector outperforms all the others. The fast computation of the proposed detector allows a multiscale approach in the change detection for operational use. The so-called multiscale change profile (MCP) is introduced to yield change information on a wide range of scales and to better characterize the appropriate scale. Two simple yet useful examples of applications show that the MCP allows the design of change indicators, which provide better results than a monoscale analysis View full abstract»

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  • Super-Resolution of Remotely Sensed Images With Variable-Pixel Linear Reconstruction

    Publication Year: 2007 , Page(s): 1446 - 1457
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1119 KB) |  | HTML iconHTML  

    This paper describes the development and applications of a super-resolution method, known as Super-Resolution Variable-Pixel Linear Reconstruction. The algorithm works combining different lower resolution images in order to obtain, as a result, a higher resolution image. We show that it can make significant spatial resolution improvements to satellite images of the Earth's surface allowing recognition of objects with size approaching the limiting spatial resolution of the lower resolution images. The algorithm is based on the Variable-Pixel Linear Reconstruction algorithm developed by Fruchter and Hook, a well-known method in astronomy but never used for Earth remote sensing purposes. The algorithm preserves photometry, can weight input images according to the statistical significance of each pixel, and removes the effect of geometric distortion on both image shape and photometry. In this paper, we describe its development for remote sensing purposes, show the usefulness of the algorithm working with images as different to the astronomical images as the remote sensing ones, and show applications to: 1) a set of simulated multispectral images obtained from a real Quickbird image; and 2) a set of multispectral real Landsat Enhanced Thematic Mapper Plus (ETM+) images. These examples show that the algorithm provides a substantial improvement in limiting spatial resolution for both simulated and real data sets without significantly altering the multispectral content of the input low-resolution images, without amplifying the noise, and with very few artifacts View full abstract»

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  • Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features

    Publication Year: 2007 , Page(s): 1458 - 1468
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1207 KB) |  | HTML iconHTML  

    Gaussian Markov random fields (GMRFs) are used to analyze textures. GMRFs measure the interdependence of neighboring pixels within a texture to produce features. In this paper, neighboring pixels are taken into account in a priority sequence according to their distance from the center pixel, and a step-by-step least squares method is proposed to extract a novel set of GMRF texture features, named as PS-GMRF. A complete procedure is first designed to classify texture samples of QuickBird imagery. After texture feature extraction, a subset of PS-GMRF features is obtained by the sequential floating forward-selection method. Then, the maximum a posteriori iterated conditional mode classification algorithm is used, involving the selected PS-GMRF texture features in combination with spectral features. The experimental results show that the performance of classifying texture samples on high spatial resolution QuickBird satellite imagery is improved when texture features and spectral features are used jointly, and PS-GMRF features have a higher discrimination power compared to the classical GMRF features, making a notable improvement in classification accuracy from 71.84% to 94.01%. On the other hand, it is found that one of the PS-GMRF texture features - the lowest order variance - is effective for residential-area detection. Some results for IKONOS and SPOT-5 images show that the integration of the lowest order variance with spectral features improves the classification accuracy compared to classification with purely spectral features View full abstract»

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  • Using Combination of Statistical Models and Multilevel Structural Information for Detecting Urban Areas From a Single Gray-Level Image

    Publication Year: 2007 , Page(s): 1469 - 1482
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1211 KB) |  | HTML iconHTML  

    With the complex building composition and imaging condition, urban areas show versatile characteristics in remote sensing images. In the literature of land-cover analysis, many algorithms utilize the features with structural information to characterize urban areas. Typically, these are more successful on some types of imagery than others, since they usually use only one kind or a few kinds of structural information. On the other hand, since levels of development in neighboring areas are not statistically independent, the multiple features (encoding the multilevel structural information) of each site in urban area depend on that of neighboring sites. In this paper, a new-come discriminative model, i.e., conditional random field (CRF), is introduced to learn the dependencies and fuse the multilevel structural information to obtain the essential detection. To meet the higher needs of some users, we introduce a two-component-based Markov random field model and show how to integrate it tightly with CRF model to refine the results from essential detection. Experiments on a wide range of images show that our algorithms are competitive with recent results in urban area detection View full abstract»

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  • ARRSI: Automatic Registration of Remote-Sensing Images

    Publication Year: 2007 , Page(s): 1483 - 1493
    Cited by:  Papers (33)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (705 KB) |  | HTML iconHTML  

    This paper presents the Automatic Registration of Remote-Sensing Images (ARRSI); an automatic registration system built to register satellite and aerial remotely sensed images. The system is designed specifically to address the problems associated with the registration of remotely sensed images obtained at different times and/or from different sensors. The ARRSI system is capable of handling remotely sensed images geometrically distorted by various transformations such as translation, rotation, and shear. Global and local contrast issues associated with remotely sensed images are addressed in ARRSI using control-point detection and matching processes based on a phase-congruency model. Intensity-difference issues associated with multimodal registration of remotely sensed images are addressed in ARRSI through the use of features that are invariant to intensity mappings during the control-point matching process. An adaptive control-point matching scheme is employed in ARRSI to reduce the performance issues associated with the registration of large remotely sensed images. Finally, a variation on the Random Sample and Consensus algorithm called Maximum Distance Sample Consensus is introduced in ARRSI to improve the accuracy of the transformation model between two remotely sensed images while minimizing computational overhead. The ARRSI system has been tested using various satellite and aerial remotely sensed images and evaluated based on its accuracy and computational performance. The results indicate that the registration accuracy of ARRSI is comparable to that produced by a human expert and improvement over the baseline and multimodal sum of squared differences registration techniques tested View full abstract»

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  • Robust Multispectral Image Registration Using Mutual-Information Models

    Publication Year: 2007 , Page(s): 1494 - 1505
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (941 KB) |  | HTML iconHTML  

    Image registration is a vital step in the processing of multispectral imagery. The accuracy to which imagery collected at multiple wavelengths can be aligned directly affects the resolution of the spectral end products. Automated registration of the multispectral imagery can often be unreliable, particularly between visible and infrared imagery, due to the significant differences in scene reflectance at different wavelengths. This is further complicated by the thermal features that exist at longer wavelengths. We develop new mathematical and computational models for robust image registration. In particular, we develop a frequency-domain model for the mutual-information surface around the optimal parameters and use it to develop a robust gradient ascent algorithm. For a robust performance, we require that the algorithm be initialized close to the optimal registration parameters. As a measure of how close we need to be, we propose the use of the correlation length and provide an efficient algorithm for estimating it. We measure the performance of the proposed algorithm over hundreds of random initializations to demonstrate its robustness on real data. We find that the algorithm should be expected to converge, as long as the registration parameters are initialized to be within the correlation-length distance from the optimum View full abstract»

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  • Multiobjective Genetic Clustering for Pixel Classification in Remote Sensing Imagery

    Publication Year: 2007 , Page(s): 1506 - 1511
    Cited by:  Papers (46)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (769 KB) |  | HTML iconHTML  

    An important approach for unsupervised landcover classification in remote sensing images is the clustering of pixels in the spectral domain into several fuzzy partitions. In this paper, a multiobjective optimization algorithm is utilized to tackle the problem of fuzzy partitioning where a number of fuzzy cluster validity indexes are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements. Real-coded encoding of the cluster centers is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Different landcover regions in remote sensing imagery have also been classified using the proposed technique to establish its efficiency View full abstract»

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    Publication Year: 2007 , Page(s): 1512
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  • IEEE Transactions on Geoscience and Remote Sensing Information for authors

    Publication Year: 2007 , Page(s): C3
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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.

 

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Meet Our Editors

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