By Topic

Geoscience and Remote Sensing, IEEE Transactions on

Issue 8 • Date Aug. 2014

Filter Results

Displaying Results 1 - 25 of 67
  • Front cover

    Publication Year: 2014 , Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (348 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Publication Year: 2014 , Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (142 KB)  
    Freely Available from IEEE
  • Table of contents

    Publication Year: 2014 , Page(s): 4497 - 4498
    Save to Project icon | Request Permissions | PDF file iconPDF (131 KB)  
    Freely Available from IEEE
  • Brightness Temperature Calculation of Lunar Crater: Interpretation of Topographic Effect on Microwave Data From Chang'E

    Publication Year: 2014 , Page(s): 4499 - 4510
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2367 KB) |  | HTML iconHTML  

    In order to quantitatively interpret topographic effect on Chang'E (CE) microwave data, a detailed method to compute brightness temperature (TB) over a lunar crater is proposed, which incorporated the effect of surface tilts. The method improves the effective solar irradiance model of the lunar surface to obtain the temperature profile of the lunar crater. The calculated TB at 37 GHz with the proposed computation method, which is based on three digital elevation models (DEMs) from different sources, are consistent with the observed TB from the CE-2 microwave radiometer. The simulated behavior of TB across crater Hercules reproduces the TB undulation observed by CE in a single swath. TB is significantly affected by the lunar dust of a lunar crater and affected by albedo and emissivity in a lesser degree. Based on the explanation with simplified models, the TB variation over a crater is proved to be significantly affected, through physical temperature, by the crater shape described by DEMs. With the simplified crater model, the amplitude of the TB oscillatory curves is proved to depend on the crater shape. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature

    Publication Year: 2014 , Page(s): 4511 - 4523
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1747 KB) |  | HTML iconHTML  

    Ship detection in high-resolution optical imagery is a challenging task due to the variable appearances of ships and background. This paper aims at further investigating this problem and presents an approach to detect ships in a “coarse-to-fine” manner. First, to increase the separability between ships and background, we concentrate on the pixels in the vicinities of ships. We rearrange the spatially adjacent pixels into a vector, transforming the panchromatic image into a “fake” hyperspectral form. Through this procedure, each produced vector is endowed with some contextual information, which amplifies the separability between ships and background. Afterward, for the “fake” hyperspectral image, a hyperspectral algorithm is applied to extract ship candidates preliminarily and quickly by regarding ships as anomalies. Finally, to validate real ships out of ship candidates, an extra feature is provided with histograms of oriented gradients (HOGs) to generate a hypothesis using AdaBoost algorithm. This extra feature focuses on the gray values rather than the gradients of an image and includes some information generated by very near but not closely adjacent pixels, which can reinforce HOG to some degree. Experimental results on real database indicate that the hyperspectral algorithm is robust, even for the ships with low contrast. In addition, in terms of the shape of ships, the extended HOG feature turns out to be better than HOG itself as well as some other features such as local binary pattern. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Unsupervised Spectral Matching Classifier Based on Artificial DNA Computing for Hyperspectral Remote Sensing Imagery

    Publication Year: 2014 , Page(s): 4524 - 4538
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3358 KB)  

    Hyperspectral remote sensing image clustering, with the large volume, high dimensions, and temporal-spatial spectral diversity, is a challenging task due to finding interesting clusters in the sparse feature space. In this paper, a novel hyperspectral clustering algorithm, namely, an unsupervised spectral matching classifier based on artificial DNA computing (UADSM), is proposed to perform the task of clustering different ground objects in specific spectral DNA feature encoding subspaces. UADSM builds up the clustering framework with the spectral encoding, optimizing, and matching mechanism by introducing the basic notions and operators of artificial DNA computing. By discretized spectral DNA feature encoding processing, the spectral shape, amplitude, and slope features of the hyperspectral data are extracted. Furthermore, the optimal clustering centers in the form of DNA strands can be found by recombining the DNA strands in the spectral DNA encoding subspace. Finally, a reasonable category for each spectral signature is automatically identified by the normalized spectral DNA similarity norm. The traditional clustering methods of k-means, ISODATA, fuzzy c-means classifier, and FCM and MoDEFC after principal component analysis transformation are provided to compare with the UADSM classifier, using Hyperspectral Digital Imagery Collection Experiment and Reflective Optics System Imaging Spectrometer hyperspectral images. The experimental results show that the UADSM classifier can achieve the best classification accuracy; hence, it is considered that the UADSM classifier is an effective clustering method for hyperspectral remote sensing imagery. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Temporal Survey of Polarimetric P-Band Scattering of Tropical Forests

    Publication Year: 2014 , Page(s): 4539 - 4547
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2019 KB) |  | HTML iconHTML  

    This paper deals with the temporal survey of the tropical forest electromagnetic scattering with a ground-based radar equipment. Installed on the top of a 55 m flux tower overlooking the Paracou forest in French Guiana, a dense primary tropical forest, the radar system uses a vertical antenna array and it is able to provide every 15 minutes P-band complex scattering matrix coefficients. The experiment has been successfully set up and it is operating since October 2011. The main goal of this campaign is to investigate the evolution of the backscattering coefficient and the temporal coherence of the tropical forest at different time scales range. Data are calibrated in relative and processed to take advantage of the largest number of independent looks. Three months of data are exploited in terms of polarimetric temporal coherence and backscattering coefficient in the rainy season and about two months in the dry period. The temporal coherence exhibits daily cycles during the consecutive dry days, whatever the period, and these cycles are perturbed by the presence of rain. Its overall time series appear clearly dependent on the period, dry or rainy, and also on the polarization. The backscattering coefficient time series exhibit also a daily cycle during consecutive dry days, very clearly in the dry period but less pronounced or absent during the rainy period. The backscattering coefficient presents an overall relatively high stability over the full period. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Spatial Adaptive Speckle Filtering Driven by Temporal Polarimetric Statistics and Its Application to PSI

    Publication Year: 2014 , Page(s): 4548 - 4557
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2705 KB) |  | HTML iconHTML  

    Persistent scatterer (PS) interferometry (PSI) techniques are designed to measure ground deformations using satellite synthetic aperture radar (SAR) data. They rely on the identification of pixels not severely affected by spatial or temporal decorrelation, which, in general, correspond to pointlike PSs commonly found in urban areas. However, in urban areas, we can find not only PSs but also distributed scatterers (DSs) whose phase information may be exploited for PSI applications. Estimation of DS parameters requires speckle filtering to be applied to the complex SAR data, but conventional speckle filtering approaches tend to mask PS information due to spatial averaging. In the context of single-polarization PSI, adaptive speckle filtering strategies based on the exploitation of amplitude temporal statistics have been proposed, which seek to avoid spatial filtering on nonhomogeneous areas. Given the growing interest on polarimetric PSI techniques, i.e., those using polarimetric diversity to increase performance over conventional single-polarization PSI, in this paper, we propose an adaptive spatial filter driven by polarimetric temporal statistics, rather than single-polarization amplitudes. The proposed approach is able to filter DS while preserving PS information. In addition, a new methodology for the joint processing of PS and DS in the context of PSI is introduced. The technique has been tested for two different urban data sets: 41 dual-polarization TerraSAR-X images of Murcia (Spain) and 31 full-polarization Radarsat-2 images of Barcelona (Spain). Results show an important improvement in terms of number of pixels with valid deformation information, hence denser area coverage. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Use of Wavelet-Based Denoising Techniques to Enhance the First-Arrival Picking on Seismic Traces

    Publication Year: 2014 , Page(s): 4558 - 4563
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1179 KB) |  | HTML iconHTML  

    Arrival time picking is a critical step in the analysis of geophysical data. This study aims at improving the standard short-term-average/long-term-average (STA/LTA) method using soft-thresholding denoising techniques based on the discrete wavelet transform. The suggested method has been first tested on simulated seismic traces. For the purpose of choosing the best parameters, we have investigated different analyzing wavelets, threshold selection rules (“minimaxi”, “universal”, “rigorousSURE”, and “heuristicSURE”), and threshold rescaling types (global and adaptive). It is shown that the best results are obtained using the Haar wavelet, the 'universal' rule, and the multiple-scale-dependent rescaling type. Afterward, simulated data sets with varying signal-to-noise ratio (SNR) have been used to compare the wave onset times picked using both STA/LTA versions (standard and denoised) and the modified energy ratio (MER) algorithm. The results show that, for data sets with high SNR values (greater than three), the MER algorithm yields the most accurate arrival times whereas, for low SNR values varying from 3 to 1.5, the denoised STA/LTA algorithm is the most effective picking algorithm. Furthermore, the picking techniques have been applied on real seismic traces recorded in the Algerian Sahara. It is again confirmed that the proposed technique provides the most reliable picking for high-noise traces. To conclude, the denoised STA/LTA algorithm is a powerful tool for identifying the first arrival for high-noise signals with SNR values lower than three and can tolerate an SNR value of about 1.5. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Accurate Gravity Compensation Method for High-Precision Airborne POS

    Publication Year: 2014 , Page(s): 4564 - 4573
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1363 KB) |  | HTML iconHTML  

    Remote sensing processing requires precise motion information provided by position and orientation system (POS), whereas gravity disturbance is normally ignored in POS solution procedure. For high-precision POS, gravity disturbance becomes a significant error source with decisive effects on the accuracy of POS. In this paper, an accurate gravity compensation method is proposed, which includes the gravity disturbance as the error states of POS Kalman filter, and the appropriate model of gravity disturbance is constructed by time series analysis coupled with the direct difference method. In verifying our gravity compensation method, POS and digital still camera combined flight experiment was conducted in 2011, where the aerial triangulation output of images was taken as a reference to evaluate the POS accuracy. Results show that the horizontal attitude accuracy of high-precision POS (0.01 °/h gyro drift) is 0.0031 ° under differential Global Positioning System condition, and the proposed method has a better performance comparable to other gravity compensation methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Aquarius RFI Detection and Mitigation Algorithm: Assessment and Examples

    Publication Year: 2014 , Page(s): 4574 - 4584
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1598 KB) |  | HTML iconHTML  

    Aquarius is an L-band radiometer system designed to map sea surface salinity from space. This is a sensitive measurement, and protection from radio frequency interference (RFI) is important for success. An initial look at the performance of the Aquarius RFI detection and mitigation algorithm is reported together with examples of the global distribution of RFI at the L-band. To protect against RFI, Aquarius employs rapid sampling (10 ms) and a “glitch” detection algorithm that looks for outliers among the samples. Samples identified as RFI are removed, and the remainder is averaged to produce an RFI-free signal for the salinity retrieval algorithm. The RFI detection algorithm appears to work well over the ocean with modest rates for false alarms (5%) and missed detection. The global distribution of RFI coincides well with population centers and is consistent with observations reported by the Soil Moisture and Ocean Salinity mission. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Improved Iterative Censoring Scheme for CFAR Ship Detection With SAR Imagery

    Publication Year: 2014 , Page(s): 4585 - 4595
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1396 KB) |  | HTML iconHTML  

    To eliminate the influence of target returns on the estimation of local sea clutter distributions, an improved iterative censoring scheme (ICS) for constant false-alarm rate detectors is proposed with two modifications. First, the proposed ICS censors out both target pixels and their four-connected neighborhood pixels from the estimation of local sea clutter distributions. Second, a novel initial detector is proposed to improve the convergence speed of ICS. The proposed initial detector, which only needs the probability of false alarms as an input parameter, is based on the sorting of all pixels under test. Experiments of ship detection with RADARSAT-2 ScanSAR wide mode images are presented to illustrate the effectiveness and improvements of the proposed ICS. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Full-Aperture SAR Data Focusing in the Spaceborne Squinted Sliding-Spotlight Mode

    Publication Year: 2014 , Page(s): 4596 - 4607
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1091 KB) |  | HTML iconHTML  

    This paper analyzes the signal properties of spaceborne squinted sliding-spotlight synthetic aperture radar (SAR). Both the squint angle and the azimuth beam steering during the whole acquisition interval will lead to the Doppler spectrum back-folding. According to the special signal properties of this mode, a new full-aperture focusing approach, which includes three major processing steps, is proposed. In this approach, the first processing step introduces an azimuth convolution and azimuth data mosaic to resolve the azimuth spectral folding phenomenon by generalizing the existing two-step focusing technique for conventional sliding-spotlight SAR data focusing. Afterward, the modified range migration algorithm is adopted to process the resulting raw data. As the azimuth time duration of the raw data is obviously reduced in the first processing step, the obtained SAR image may be back-folded in azimuth. The final azimuth postfiltering step is to resolve the possible aliased SAR image without any azimuth data extension. The proposed full-aperture focusing processor is efficient since only a limited azimuth data extension is required to resolve the back-folded Doppler spectrum and SAR image. Imaging results on simulated raw data validate the proposed imaging approach. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Compressed Sensing Radar Imaging With Compensation of Observation Position Error

    Publication Year: 2014 , Page(s): 4608 - 4620
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1510 KB) |  | HTML iconHTML  

    Compressed sensing (CS) based radar imaging requires the use of a mathematical model of the observation process. Inaccuracies in the observation model may cause defocusing in the reconstructed images. In the observation process, the observation positions are usually not known perfectly. Imperfect knowledge of the observation positions is a major source of model errors in imaging. In this paper, a method is proposed to compensate the observation position errors in CS-based radar imaging. Instead of treating the observation-position-induced model errors as phase errors in the data, the proposed method can determine the observation position errors as part of the imaging process. It uses an iterative algorithm, which cycles through steps of target reconstruction and observation position error estimation and compensation. The proposed method can estimate the observation position errors accurately, and the reconstruction quality of the target images can be improved significantly. Simulation results and experimental results from rail-mounted radar and airborne synthetic aperture radar are presented to show the effectiveness of the proposed method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A New Accuracy Assessment Method for One-Class Remote Sensing Classification

    Publication Year: 2014 , Page(s): 4621 - 4632
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2023 KB) |  | HTML iconHTML  

    In one-class remote sensing classification, users are only interested in classifying one specific land type (positive class), without considering other classes (negative class). Previous researchers have proposed different one-class classification methods without requiring negative data. An appropriate accuracy measure is usually needed to tune free parameters/threshold and to evaluate the classification result. However, traditional accuracy measures, such as the kappa coefficient and F-measure (F), require both positive and negative data, and hence, they are not applicable for positive-only data. In this paper, we investigate a new accuracy assessment method that does not require negative data. Two new statistics Fpb (proxy of F-measure based on positive-background data) and Fcpb (prevalence-calibrated proxy of F-measure based on positive-background data) can be calculated from a modified confusion matrix, where the observed negative data are replaced by background data. To investigate the effectiveness of the new method, we produced different one-class classification results using two scenes of aerial photograph, and the accuracy values were evaluated by Fpb, Fcpb, kappa coefficient, and F. The effectiveness of F pb in model and threshold selection was investigated as well. Experimental results show that the behaviors of Fpb, Fcpb, F, and kappa coefficient are similar, and they all rank the models by accuracy similarly. In model and threshold selection, the classification accuracy values produced by maximizing Fpb and F are similar, and they are higher than those produced by setting an arbitrary rejection fraction. Therefore, we conclude that the new method is effective in model selection, threshold selection, and accuracy assessment, and it will have important applications in one-class remote sensing classification since negative data are not needed. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Despeckling and Information Extraction From SLC SAR Images

    Publication Year: 2014 , Page(s): 4633 - 4649
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3549 KB) |  | HTML iconHTML  

    This paper presents an information extraction and image enhancement technique using single-look complex (SLC) synthetic aperture radar data. The novelty of this method is the proposed complex-domain despeckling stage. Tikhonov-like optimization is used for minimizing the cost function, which consists of a Gauss-Markov random field (GMRF) prior. The GMRF model is used for texture modeling. The texture parameters of the GMRF are estimated using the evidence maximization framework. The experimental results showed that despeckled SLC images have well-preserved textural features, structures, and point scatterers. The phase of the reconstructed image is well preserved and provides good-quality interferograms of high-resolution spotlight images. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Forest Canopy Height Extraction in Rugged Areas With ICESat/GLAS Data

    Publication Year: 2014 , Page(s): 4650 - 4657
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1400 KB) |  | HTML iconHTML  

    Geoscience Laser Altimeter System data have been widely used in forest canopy height extraction. It is still challenging over rugged areas. In this paper, we propose a forest canopy height extraction method consisting of the Savitzky-Golay filter and fitting, Sigbeg determination based on the fitting results, and slope correction for rugged areas, particularly for slopes ranging from 5 ° to 15 °. The method was applied to both the Xinlin Forest, China, and Santa Rosa National Park, Costa Rica. The performance of this method was validated by field measurement and Laser Vegetation Imaging Sensor data. The goodness of fit (R2) reached 0.73 and 0.78, respectively, and root-mean-squared errors (RMSEs) were 2.27 and 3.75 m over the two areas, respectively. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Orthonormalized Volume Magnetic Source Model for Discrimination of Unexploded Ordnance

    Publication Year: 2014 , Page(s): 4658 - 4670
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1074 KB) |  | HTML iconHTML  

    We introduce a fast and accurate numerical technique for the solution of electromagnetic induction sensing problems called the orthonormalized volume magnetic source model. The model assumes that the secondary magnetic field measured by a sensor originates from a set of magnetic dipole sources distributed over a volume that coincides with the interrogated area. The Green functions associated with the responding sources are turned into an orthonormal basis using a generalization of the Gram-Schmidt method, enabling one to determine the sources' strengths directly from measured data without having to invert large and potentially ill-conditioned matrices. The method treats multitarget cases naturally and robustly. Several examples are presented to illustrate the applicability of the method in the discrimination of unexploded ordnance (UXO). In particular, we analyze data taken by the Time-Domain Electromagnetic Multisensor Towed Array Detection System sensor array at a test stand and during a blind test administered at a UXO live site. The method is highly successful in distinguishing UXO from among other UXO and from accompanying clutter. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Sensitivity of L-Band Radar Backscatter to Forest Biomass in Semiarid Environments: A Comparative Analysis of Parametric and Nonparametric Models

    Publication Year: 2014 , Page(s): 4671 - 4685
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1470 KB) |  | HTML iconHTML  

    This paper investigated the effectiveness of frequently used parametric and nonparametric models for biomass retrieval from L-band radar backscatter. Two areas, one in Spain and one in Australia, characterized by different tree species, forest structure, and field sampling designs were selected to demonstrate that retrieval error metrics are similar for different local conditions and sampling characteristics. A mixed-model retrieval strategy was proposed to reduce the overall (i.e., across the entire biomass range) as well as by-biomass-interval errors. Significant relationships were found between aboveground biomass and radar backscatter with most of the backscatter dynamic range being limited to a fairly low range of biomass values ( t/ha) in both study areas. Biomass retrieval errors were largely similar for all parametric and nonparametric models tested. However, some parametric models consistently provided lower correlation between the observed and the predicted biomass while nonparametric models generally provided an unbiased estimation. A mixed-model retrieval strategy was shown to reduce biomass estimation errors by up to 15%. Biomass retrieval errors were highly variable within the L-band sensitivity interval, suggesting that overall accuracy estimates should be used with care, particularly for low biomass intervals ( t/ha) where surface scattering could dominate the total backscatter. Despite exhibiting the highest dynamic range, low biomass areas were characterized by the highest estimation errors (in excess of 80%). Conversely, relative estimation errors were as low as 20%-35% for the 30-75 t/ha biomass intervals, while at higher biomass levels, the estimation error increased due to signal saturation. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Validation Study of an Improved SWIR Iterative Atmospheric Correction Algorithm for MODIS-Aqua Measurements in Lake Taihu, China

    Publication Year: 2014 , Page(s): 4686 - 4695
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1553 KB) |  | HTML iconHTML  

    We have presented an improved short-wave infrared (SWIR)-based iterative algorithm for the atmospheric correction (AC) of Moderate Resolution Imaging Spectroradiometer (MODIS) data over Lake Taihu, China. The algorithm was validated by means of matchup comparison between MODIS-retrieved and in situ remote sensing reflectances (Rrs). Four examples of the matchup comparison were first carried out for the observation stations within a ±5-min time window of MODIS overpass and field measurements. It is shown in the examples that the retrieved Rrs spectra compare reasonably well with the in situ measurements not only over relatively clear waters (with Rrs(859) about 0.0014 sr-1) but also over turbid waters (with Rrs(859) about 0.013 sr-1). The matchup comparison was further carried out for a total of 54 observation stations within a ±2-h time window, indicating that the AC algorithm has good performance for producing water spectra from MODIS data over Lake Taihu. The development of an algal bloom event has been monitored using MODIS-measured Rrs(443) and Rrs(859), showing that MODIS data, combined with the AC algorithm, can be a useful tool for monitoring the water quality of Lake Taihu. The SWIR iterative algorithm, along with the chlorophyll-a concentration (Chl-a) retrieval model using red to near-infrared bands, has the potential of monitoring Chl-a quantitatively and providing useful information for decision makers to manage the water environment and to prepare for events as algal blooms. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Improved Iterative Algorithm for 3-D Ionospheric Tomography Reconstruction

    Publication Year: 2014 , Page(s): 4696 - 4706
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2216 KB) |  | HTML iconHTML  

    The computerized ionospheric tomography usually involves solving an ill-posed inversion problem. The sparsity of Global Positioning System (GPS) stations and the limitation of projection angles lead to insufficient data acquisition, thereby preventing the accurate reconstruction of ionospheric-electron-density distributions. In this paper, we investigate and propose a 3-D iterative reconstruction algorithm based on the minimization of total variation under quiescent and disturbed ionospheric conditions. Numerical experiments on GPS simulation data and real data are discussed. In contrast to the improved algebraic reconstruction technique, the proposed algorithm exhibits significantly reconstruction accuracy. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Two-Color Satellite Laser Ranging Measurements at 10 Hz and 100 Hz at TIGO

    Publication Year: 2014 , Page(s): 4707 - 4716
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1684 KB) |  | HTML iconHTML  

    In this paper, a detailed comparative performance analysis of the two-color satellite laser ranging (SLR) systems of the Transportable Integrated Geodetic Observatory (TIGO) is presented. The study is based on four years of continuous measurement data and a comparison of two different layouts of the laser system. The focus lies on a quantitative analysis of measurement precision, range accuracy, and data production for the two laser system layouts. Main findings include a significant gain in temporal stability due to removal of active elements in the oscillator, an improvement of range measurement accuracy by a factor of 2, and an important increase in data productivity. The analysis presented here provides a valuable input for the design of future SLR systems, as well as related topics such as time transfer applications and optical communications to satellites. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Atmospheric Boundary Layer Height Monitoring Using a Kalman Filter and Backscatter Lidar Returns

    Publication Year: 2014 , Page(s): 4717 - 4728
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1447 KB) |  | HTML iconHTML  

    A solution based on a Kalman filter to trace the evolution of the atmospheric boundary layer (ABL) sensed by a ground-based elastic-backscatter tropospheric lidar is presented. An erf-like profile is used to model the mixing-layer top and the entrainment-zone thickness. The extended Kalman filter (EKF) enables to retrieve and track the ABL parameters based on simplified statistics of the ABL dynamics and of the observation noise present in the lidar signal. This adaptive feature permits to analyze atmospheric scenes with low signal-to-noise ratios (SNRs) without the need to resort to long-time averages or range-smoothing techniques, as well as to pave the way for future automated detection solutions. First, EKF results based on oversimplified synthetic and experimental lidar profiles are presented and compared with classic ABL estimation quantifiers for a case study with different SNR scenarios. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Hyperspectral Image Restoration Using Low-Rank Matrix Recovery

    Publication Year: 2014 , Page(s): 4729 - 4743
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5077 KB) |  | HTML iconHTML  

    Hyperspectral images (HSIs) are often degraded by a mixture of various kinds of noise in the acquisition process, which can include Gaussian noise, impulse noise, dead lines, stripes, and so on. This paper introduces a new HSI restoration method based on low-rank matrix recovery (LRMR), which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes. By lexicographically ordering a patch of the HSI into a 2-D matrix, the low-rank property of the hyperspectral imagery is explored, which suggests that a clean HSI patch can be regarded as a low-rank matrix. We then formulate the HSI restoration problem into an LRMR framework. To further remove the mixed noise, the “Go Decomposition” algorithm is applied to solve the LRMR problem. Several experiments were conducted in both simulated and real data conditions to verify the performance of the proposed LRMR-based HSI restoration method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Improving Argos Doppler Location Using Multiple-Model Kalman Filtering

    Publication Year: 2014 , Page(s): 4744 - 4755
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1942 KB) |  | HTML iconHTML  

    The Argos service was launched in 1978 to serve environmental applications, including oceanography, wildlife tracking, fishing vessel monitoring, and maritime safety. The system allows for worldwide near-real-time positioning and data collection of platform terminal transmitters (PTTs). The positioning of the PTTs is achieved by exploiting the Doppler shift in the carrier frequency of the transmitter as recorded by satelliteborne Argos receivers. Until March 15, 2011, a classical nonlinear least squares estimation technique was systematically used to estimate Argos positions. Since then, a second positioning algorithm using a multiple-model Kalman filter was implemented in the operational Argos positioning software. This paper presents this new algorithm and analyzes its performance using a large data set obtained from over 200 mobiles carrying both an Argos transmitter and a GPS receiver used as ground truth. The results show that the new algorithm significantly improves the positioning accuracy, particularly in difficult conditions (for class-A and class-B locations, in the Argos terminology). Moreover, the new algorithm enables the retrieval of a larger number of estimated positions and the systematic estimation of the location error. View full abstract»

    Open Access

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