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Geoscience applications often produce sizable datasets that are vector-valued and increasingly in need of compression algorithms to reduce storage and transmission burdens, particularly when the data are time-varying. In this paper, several advanced interframe-compression techniques are extended from the traditional realm of natural video to the coding of time-varying vector fields. Although similar to natural video in some respects, time-varying vector-field sequences often possess complex temporal evolution of vector-valued features that are important to the analytic quality of the data yet defy the simple motion models widely employed for natural video. To improve coding performance, motion compensation with reduced resolution is proposed such that motion compensation is applied only at low spatial resolution, while high-resolution information, for which the motion model fails, is intraframe coded with no temporal decorrelation. In empirical results on datasets of ocean-surface winds, this reduced-resolution motion-compensation technique results in significant performance improvement and greater feature preservation. [ABSTRACT FROM AUTHOR]

Range and sill are two important parameters of a variogram. Their extraction usually Involves experimental fitting of variograms using models specified by the analyst and requires much use of trial and error. The objective of this paper is to desâgn an algorithm for extracting the range and sill of a variogram automatically without fitting a model Combined with the semivariance at the lag of one pixel (γ1), the extracted range and sift were applied to the textural classification of a panchromatic IKONOS image over Xichang, Sichuan Province, China. Results show that any of these three parameters can lead to the increase of the classification accuracy. When all three parameters were used with the raw image data, the average kappa statistic for five window sizes increased from 0.24 to 0.76, indicating promise of the range and sill in texture classification. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate how the complementarity between low earth orbit (LEO) microwave (MW) and geostationary earth orbit (GEO) Infrared (IR) radiometric measurements can be exploited for satellite rainfall detection and estimation. Rainfall retrieval Is pursued at the space-time scale of typical geostationary observations, that Is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The basic idea behind the investigated statistical integration methods follows an established approach consisting in using the satellite MW-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne La measurements on sufficiently limited sub regions and time windows. The proposed methodologies are focused on new statistical approaches, namely the multivariate probability matching (MPM) and variance-constrained multiple regression (VMR). The MPM and VMR methods are rigorously formulated and systematically analyzed in terms of relative detection and estimation accuracy and computing efficiency in order to demonstrate the potentiality of the proposed MW-La combined rainfall algorithm (MICRA), three case studies ire discussed, two on a global scale on November 1999 and 2000 and one over the Mediterranean area. A comprehensive set of statistical parameters for detection and estimation assessment is introduced to evaluate the error budget. For a comparative evaluation, the analysis of these case studies has been extended to similar techniques available in literature. [ABSTRACT FROM AUTHOR]

This paper is devoted to the application of the Semi-Analytical Cloud Retrieval Algorithm (SACURA) to the cloud-top height determination using data from the Global Ozone Measurement Experiment (GOME) instrument onboard Earth Remote Sensing satellite (ERS-2). In particular, measurements of the top-of-atmosphere reflectance in the oxygen absorption A-band are used. The technique is based on the asymptotic radiative transfer theory as applied to studies of the oxygen absorption bands in reflected light. Our approach is valid for optically thick clouds with cloud optical thickness larger than approximately 5. The accuracy of the algorithm is checked against independent retrieval techniques for completely cloudy pixels. In particular, the dose coincidence with data obtained from the Along Track Scanning Radiometer (ATSR-2) onboard ERS-2 is found. Some results of retrievals using these different instruments disagree (up to 2 km). This is explained by the strong horizontal inhomogeneity of clouds under investigation, which is not accounted by the SACURA or, possibly, by well-known problems of infrared techniques as applied to low-level clouds. The effective cloud geometrical thickness 1 is also retrieved. This parameter is defined as the geometrical thickness of a vertically homogeneous cloud, which allows for the minimization of differences between observed and calculated top-of-atmosphere reflectance spectra. For inhomogeneous clouds, the value of 1 differs from a real cloud geometrical thickness, but it gives us an indication of the possible existence of the multilayered cloud system in the field of view of the optical instrument. [ABSTRACT FROM AUTHOR]

In this paper, we proposed an unsupervised terrain and land-use classification algorithm using polarimetric synthetic aperture radar data. Unlike other algorithms that classify pixels statistically and ignore their scattering characteristics, this algorithm not only uses a statistical classifier, but also preserves the purity of dominant polarimetric scattering properties. This algorithm uses a combination of a scattering model-based decomposition developed by Freeman and Durden and the maximum- likelihood classifier based on the complex Wishart distribution. The first step is to apply the Freeman and Durden decomposition to divide pixels into three scattering categories: surface scattering, volume scattering, and double-bounce scattering. To preserve the purity of scattering characteristics, pixels in a scattering category are restricted to be classified with other pixels in the same scattering category. An efficient and effective class initialization scheme is also devised to initially merge clusters from many small clusters in each scattering category by applying a merge criterion developed based on the Wishart distance measure. Then, the iterative Wishart classifier is applied. The stability in convergence is much superior to that of the previous algorithm using the entropy/anisotropy/Wishart classifier. Finally, an automated color rendering scheme is proposed, based on the classes' scattering category to code the pixels to resemble their natural color. This algorithm is also flexible and computationally efficient. The effectiveness of this algorithm is demonstrated using the Jet Propulsion Laboratory's AIRSAR and the German Aerospace Center's (DLR) E-SAR L-band polarimetric synthetic aperture radar images. [ABSTRACT FROM AUTHOR]

Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for autonomous and supervised endmember extraction from hyperspectral data. Due to a lack of commonly accepted data and quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared by using a unified scheme. In this paper, we present a comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information. The algorithms considered in this study represent substantiate different design choices. A database formed by simulated and real hyperspectral data collected by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) is used to investigate the impact of noise, mixture complexity, and use of radiance/reflectance data on algorithm performance. The results obtained indicate that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied. [ABSTRACT FROM AUTHOR]

Retrieving aerosol properties from satellite remote sensing over a bright surface is a challenging problem in the research of atmospheric and land applications. In this paper we propose a new approach to retrieve aerosol properties over surfaces such as and, semiarid, and urban areas, where the surface reflectance is usually very bright in the red part of visible spectrum and in the near infrared, but is much darker in the blue spectral region (i.e., wavelength <500 nm). In order to infer atmospheric properties from these data, a global surface reflectance database of 0.1° latitude by 0.1° longitude resolution was constructed over bright surfaces for visible wavelengths using the minimum reflectivity technique (e.g., finding the clearest scene during each season for a given location). The aerosol optical thickness and aerosol type are then determined simultaneously in the algorithm using lookup tables to match the satellite observed spectral radiances. Examples of aerosol optical thickness derived using this algorithm over the Sahara Desert and Arabian Peninsula reveal various dust sources, which are important contributors to airborne dust transported over long distances. Comparisons of the satellite inferred aerosol optical thickness and the values from ground-based Aerosol Robotic Network (AERONET) sun/sky radiometer measurements indicate good agreement (i.e., within 30%) over the sites in Nigeria and Saudi Arabia. This new algorithm, when applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field of view Sensor (SeaWiFS), and Global Imager (GLI) satellite data, will provide high spatial resolution (∼1 km) global information of aerosol optical thickness over bright surfaces on a daily basis. [ABSTRACT FROM AUTHOR]

Signal processing algorithms for hand-held mine detection sensors are described. The goals of the algorithms are to provide alarms to a human operator indicating the likelihood of the presence of a buried mine. Two modes of operations are considered: search mode and discrimination mode. Search mode generates an initial detection at a suspected location and discrimination mode confirms that the suspected location contains a land mine. Search mode requires that the signal processing algorithm generate a detection confidence value immediately at the current sample location and no delay in producing an alarm confidence is tolerable. Search mode detection has a high false-alarm rates Discrimination mode allows the operator to interrogate the entire suspected location to eliminate false alarms. It does not require that the signal processing algorithm produce an alarm confidence immediately for the current sample location, but rather allows the system to process all the data acquired over the region before producing an alarm. This paper proposes discrimination mode processing algorithms for metal detectors (MDs), or electromagnetic induction sensors (EMIs), ground-penetrating radars (GPRs), and their fusion. The MD discrimination mode algorithm employs a model-based approach and uses the target model parameters to discriminate between mines and clutter objects. The GPR discrimination mode algorithm uses the consistency of detection as well as the shape of the detection peaks over several sweeps to improve the discrimination accuracy. The performances of the proposed algorithms were examined on a dataset collected at a government test site, and performance was compared with baseline techniques. Experimental results showed that the proposed method can reduce the probability of false alarm by as much as 70% at a 100% correct detection rate and performed comparable to the best human operator on a blind test with data collected at approximately 1000 locations. [ABSTRACT FROM AUTHOR]

This paper describes an algorithm aimed at monitoring the soil moisture and the growth cycle of wheat fields using radar data. The algorithm is based on neural networks trained by model simulations and multitemporal ground data measured on fields taken as a reference. The backscatter of wheat canopies is modeled by a discrete approach, based on the radiative transfer theory and including multiple scattering effects. European Remote Sensing satellite synthetic aperture radar signatures and detailed ground truth, collected over wheat fields at the Great Driffield (U.K.) site, are used to test the model and train the networks Multitemporal, multifrequency data collected by the Radiometer-Scatterometer (RASAM) instrument at the Central Plain site are used to test the retrieval algorithm. [ABSTRACT FROM AUTHOR]

Multitemporal and multisatellite studies or comparisons between satellite data and local ground measurements require nowadays precise and automatic geometric correction of satellite images. This paper presents a fully automatic geometric correction system capable of georeferencing satellite images with high accuracy. An orbital prediction model, which provides initial earth locations, is combined with the proposed automatic contour-matching technique. This combination allows correcting the low-frequency error component, mainly due to timing and orbital model errors, as well as the high-frequency error component, due to variations in the spacecraft's attitude. The approach aims at exploiting the maximum reliable information in the image to guide the matching algorithm. The contour-matching process has three main steps: 1) estimation of the gradient energy map (edges) and detection of the cloudless (reliable) areas; 2) initialization of the contours positions; 3) estimation of the transformation parameters (affine model) using a contour optimization approach. Three different robust and automatic algorithms are proposed for optimization, and their main features are discussed. Finally, the performance of the three proposed algorithms is assessed using a new error estimation technique applied to Advanced Very High Resolution Radiometer (AVHRR), Sea-viewing Wide Field of view Sensor (Sea WiFS), and multisensor AVHRR-Sea WifS imagery. [ABSTRACT FROM AUTHOR]

This paper introduces a machine-learning approach to satellite ocean color sensor cross calibration. The cross-calibration objective is to eliminate incompatibilities among sensor data from different missions and produce merged daily global ocean color coverage. The approach is designed and investigated using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard of the Terra satellite and Sea-viewing Wide Field-of-view Sensor (Sea WiFS). Data from these two sensors show apparent discrepancies originating from differences in sensor design, calibration, processing algorithms, and from the rate of change in the atmosphere and ocean within 1(½) h between sensor imaging of the same regions on the ground. The discrepancies have complex, noisy, and often contradictory time and space variabilities. Support vector machines are used to bring MODIS data to the Sea WiFS representation where Sea WiFS data are considered to exemplify a consistent ocean color baseline. Support vector machines are effective in learning and resolving convoluted data relationships between the two sensors given a variety of bio-optical, atmospheric, viewing geometry, and ancillary information. The method works accurately in low chlorophyll waters and shows a potential to eliminate sensor problems, such as scan angle dependencies and seasonal and spatial trends in data. The results illustrate that MODIS and Sea WiFS differences are noisy and highly variable, which makes it difficult to extrapolate the cross-calibration knowledge onto new time and space domains and to define representative global ocean color datasets for support vector machine training. [ABSTRACT FROM AUTHOR]

This paper presents an almost unsupervised fusion algorithm on linear features (LF) extraction in synthetic aperture radar (SAR) interferometric data, in particular for mangroves/shorelines and thin internal channels. The spatial information on LFs is first extracted in the coherence image, where they are wider and more visible: water regions (in particular thin internal channels) are dark areas (low coherence) due to the temporal decorrelation of backscattering signals in these and surrounding regions, whereas conventional vegetation regions are brighter areas (high coherence). These approximate locations of LFs are further refined by using the edge map coming from a semantic fuzzy fusion of the coefficient of variation (CV) and the ratio of local means (RLM) measured in the amplitude image. The final detection of LFs is then performed by merging the two fuzzy inputs: the spatial information and the edge location map. The membership degree statistics of CV and RLM semantic fusion measures are introduced in order to illustrate the location detection ability. The originality of this method in comparison with conventional approaches is in. the fusion scheme that follows the interpreter behavior by using first the coherence image for a fuzzy detection where thin LFs are more visible, but have low location accuracy, and then the amplitude image where they are poorly visible, but with higher location accuracy, to obtain improved results. A quantitative performance evaluation is also presented. The method has been applied on real interferometric SAR images from European Remote Sensing satellites over the western part of Cameroon. [ABSTRACT FROM AUTHOR]

Accurate registration of multitemporal remote sensing images is essential for various change detection applications. Mutual information has recently been used as a similarity measure for registration of medical images because of its generality and high accuracy. Its application in remote sensing is relatively new. There are a number of algorithms for the estimation of joint histograms to compute mutual information, but they may suffer from interpolation-induced artifacts under certain conditions. In this paper, we investigate the use of a new joint histogram estimation algorithm called generalized partial volume estimation (GPVE) for computing mutual information to register multitemporal remote sensing images. The experimental results show that higher order GPVE algorithms, have the ability to significantly reduce interpolation-induced artifacts. In addition, mutual information-based image registration performed using the GPVE algorithm produces-better registration consistency than the other two popular similarity measures, mean squared difference (MSD) and normalized cross correlation (NCC), used for the registration of multitemporal remote sensing images. [ABSTRACT FROM AUTHOR]

A new set of algorithms has been developed to estimate spectral reflectance in remote sensing imagery. These algorithms are called surface prior information reflectance estimation (SPIRE) algorithms and estimate surface spectral reflectance using prior spatial and spectral information about the surface reflectance. This paper describes SPIRE algorithms that employ spatial processing of single-channel data to estimate local changes in spectral reflectance under spatially and spectrally varying multiplicative and additive noise caused by variations in illumination and atmospheric effects. Rather than modeling the physics of the atmosphere and illumination (using a physics-based code such as the Atmospheric Removal (ATREM) program), or using ground truth spectra at known locations to compensate for these effects [using the empirical line method (ELM)], prior information about the low spatial frequency content of the scene in each spectral channel is used instead, HYDICE visible near-infrared shortwave infrared (VNIR-SWIR) hyperspectral data were used to compare the performance of SPIRE, ATREM, and ELM atmospheric compensation algorithms. The spatial SPIRE algorithm performance was found to be nearly identical to the ELM ground. truth-based results, while spatial SPIRE performed better than ATREM overall and significantly better under high clouds and haze. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) raw signal simulation is a powerful tool for designing new sensors, testing processing algorithms, planning missions, and devising inversion algorithms. In this paper, a spotlight SAR raw signal simulator for distributed targets is presented. The proposed procedure is based on a Fourier domain analysis: a proper analytical reformulation of the spotlight SAR raw signal expression is presented. It is shown that this reformulation allows us to design a very efficient simulation scheme that employs fast Fourier transform codes. Accordingly, the computational load is dramatically reduced with respect to a time-domain simulation and this, for the first time, makes spotlight simulation of extended scenes feasible. [ABSTRACT FROM AUTHOR]

It is known that the extended Born approximation (ExBorn) is much faster than the method of moments (MoM) in the study of electromagnetic scattering by three-dimensional (3-D) dielectric objects, while iris much more accurate titan the Bern approximation at low frequencies. Hence, it is more applicable in the low-frequency numerical simulation tools. However, the conventional ExBorn is still too slow to solve large-scale problems because it requires O(N²) computational load, where N is the number of unknowns. In this paper, a fast ExBorn algorithm is proposed for the numerical simulation of 3-D dielectric objects buried in a lossy earth. When the buried objects are discretized with uniform rectangular mesh and the Green's functions are extended appropriately, the computational load eau be reduced to O(N log N) using the cyclic convolution, cyclic correlation, and fast Fourier transform (FFT). Numerical analysis shows that the fast ExBorn provides good approximations if the buried target has a small or moderate contrast. If the contrast is large, however, ExBorn will be less accurate. In this case, a preconditioned conjugate-gradient FFT (CG-FFT) algorithm is developed, where the solution of the last ExBorn is chosen as the initial guess and the preconditioner. Numerical results are given to test the validity and efficiency of the fast algorithms. [ABSTRACT FROM AUTHOR]

The two main elements of the Atmospheric Infrared Sounder Radiative Transfer Algorithm (AIRS-RTA) are described in this paper: 1) the fast parameterization of the atmospheric transmittances that are used to perform the AIRS physical retrievals and 2) the spectroscopy used to generate the parameterized transmittances. We concentrate on those aspects of the spectroscopy that are especially relevant for temperature and water vapor retrievals. The AIRS-RTA is a hybrid model in that it parameterizes most gases on a fixed grid of pressures, while the water optical depths are parameterized on a fixed grid of water amounts. Water vapor, ozone, carbon monoxide, and methane profiles can be varied, in addition to the column abundance of carbon dioxide. [ABSTRACT FROM AUTHOR]

The thermal structure of trace gases, their distribution in the atmosphere, and their circulation mechanisms result from a complex interplay between radiative, physical, and dynamical processes. Neural-network algorithms can be a useful tool to face such complexities in retrieval operations. In this paper, their potentialities have been exploited to design real-time procedures for the estimation of vertical profiles of ozone concentration from spectral radiances measured by GOME, the first instrument of the European Space Agency capable of monitoring global distribution of ozone and other trace gases. [ABSTRACT FROM AUTHOR]

The primary objectives of this response communication are to provide insight into the use of spectral vegetation indices (SVIs) and a user's perspective on the uncertainty in SVI values, especially when these are derived from multiple sensors. We review how two papers in this special issue address uncertainty, and we explore two practical applications of SV1 products and how comprehensive quantification and spatially explicit visualization of uncertainty could enhance their use. Although researchers identify the causes of uncertainties in SVIs, there has been little advancement in connecting and integrating the associated uncertainties inherent to all steps of the processing and model chains (e.g., data capture, data input and SVI generation). Cross-comparison of uncertainty assessment is challenging to the end-product user because reporting of uncertainty tends to be research or data product-specific with limited emphasis on facilitating the interpretation of uncertainty associated with algorithm and processing quality for use by managers or decision makers. Consequently, the confidence in these data is often based on experience and visual confirmation of the spatial and temporal consistency in SVI imagery and time-series data. Although the level of accuracy required varies depending on use, overall product quality assurance and a comprehensive, site-specific uncertainty assessment bundled with SVI data fields could mean the difference between using SVIs to report on spatial-temporal patterns versus using these data to make natural resource management decisions. [ABSTRACT FROM AUTHOR]

A recent paper discussed the orthogonal subspace projection (OSP) algorithm. That paper advocated the use of OSP for target detection and material abundance estimation for mixed pixels in hyperspectral images. However, it was proved in an older paper that an algorithm called the constrained signal detector (CSD) generally outperforms the OSP for the applications of target detection and abundance estimation. The purpose of this correspondence is to review the differences in detection and estimation performance between OSP and CSD. [ABSTRACT FROM AUTHOR]

The automatic seismic signal detection constitutes a very interesting and challenging task. The main difficulty in solving this problem is attributed to the fact that both the statistical properties of seismic noise and the characteristics of the recorded events are in general unknown. In this paper, by exploiting the particular nature of the signals we are treating, as well as a number of very interesting properties possessed by exchangeable random variables, we formulate the problem at hand as a record segmentation one and propose the use of two functionally linked test statistics for its efficient and robust solution, in a two-step procedure. By following this approach, we succeed not only in detecting a seismic wave arrival, but also in identifying the entire interval occupied by the signal, while minimizing the number of the required parameters. The performance of the proposed technique is confirmed by a series of experiments, both in synthetic and real seismic data sets. [ABSTRACT FROM PUBLISHER]

In the above titled paper (ibid., vol. 48, no. 9, pp. 3391-3407, Sep. 10), there is an error in equation (A-5). The correct form is presented here. [ABSTRACT FROM AUTHOR]