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TOC Alert for Publication# 36 2017January 19<![CDATA[Front Cover]]>552C1C1503<![CDATA[IEEE Transactions on Geoscience and Remote Sensing publication information]]>552C2C284<![CDATA[Table of contents]]>5526171224201<![CDATA[Influences of Leaf-Specular Reflection on Canopy BRF Characteristics: A Case Study of Real Maize Canopies With a 3-D Scene BRDF Model]]>in situ measured BRFs over real maize canopies. The results show that ignorance of leaf-specular reflection can result in up to 50% of relative error in the blue band (435.8 nm). A series of maize canopies with different leaf angle distributions (LADs) is reconstructed to investigate the effect of five major biophysical/geometrical parameters such as leaf area index, LAD, leaf surface property, view direction, and solar zenith angle on leaf-specular reflection contributions to the canopy BRF. It is demonstrated that increasing the incident solar zenith angle and decreasing the mean leaf angle impact the angular distribution of the canopy BRF more significantly than other factors. The cumulative hemispherical relative and absolute errors of canopy BRF caused by the leaf-specular reflection are often too large to be ignored, even for canopies with rough surface leaves. Moreover, the relative error of BRF in visible waveband shows that, in general, leaf-specular reflection has a large impact than that in near-infrared waveband. However, such impact can be sufficiently accounted for by even just consideration of the first-order leaf-specular reflection in canopy reflectance calculation, leading to a substa-
tial improvement in simulation accuracy for most vegetation canopies.]]>5526196311549<![CDATA[Precise Real-Time Detection of Nonforested Areas With UAVs]]> %) and processing times ( s). The method presented copes well with detecting regional irregularities and reduces frequent issues of nondetection, as well as false positives caused by intensity changes, shadows, and/or partial occlusions. The low processing times achieved with the proposed method allow real-time applications for low-cost unmanned aerial vehicle and unmanned aircraft systems with conventional camera equipment.]]>5526326443884<![CDATA[Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification]]>fully convolutional architecture and demonstrate its relevance to the dense classification problem. We then address the issue of imperfect training data through a two-step training approach: CNNs are first initialized by using a large amount of possibly inaccurate reference data, and then refined on a small amount of accurately labeled data. To complete our framework, we design a multiscale neuron module that alleviates the common tradeoff between recognition and precise localization. A series of experiments show that our networks consider a large amount of context to provide fine-grained classification maps.]]>55264565711453<![CDATA[Optimizing Object, Atmosphere, and Sensor Parameters in Thermal Hyperspectral Imagery]]>5526586701924<![CDATA[Adaptive Spectral–Spatial Compression of Hyperspectral Image With Sparse Representation]]>5526716824567<![CDATA[Forward a Small-Timescale BRDF/Albedo by Multisensor Combined BRDF Inversion Model]]> ), for a robust BRDF/albedo retrieval. The performance of the MCBI is assessed by comparisons with MODIS BRDF/albedo product and the in situ measurement. The results show that the highly frequent angular sampling with four sensors allows for a full retrieval of BRDF/albedo with a shorter accumulation period of 8 and 4 days. The reduces the uncertainties when using different sensors’ reflectance and allows for a high-quality BRDF/albedo retrieval. It reveals that the MCBI has the potential to generate a multisensor-based BRDF/albedo on a small timescale. The MCBI is a key algorithm for the BRDF/albedo product in China’s multisource data synergized quantitative remote sensing production system and operationally implemented to generate a global product.]]>5526836975693<![CDATA[Tomographic Imaging of Fjord Ice Using a Very High Resolution Ground-Based SAR System]]>55269871424241<![CDATA[Hyperspectral Image Classification via Kernel Basic Thresholding Classifier]]> smoothing technique on the guidance image, which provides additional improvements. Experimental results on publicly available HSI data sets showed that the proposal and its spatial extension yield better classification results as compared with linear similarity-based BTC, nonlinear kernel-based support vector machines, kernel orthogonal matching pursuit, recently proposed logistic regression via splitting and augmented Lagrangian, and their spatial extensions.]]>5527157283026<![CDATA[Multiview Marker-Free Registration of Forest Terrestrial Laser Scanner Data With Embedded Confidence Metrics]]>5527297417588<![CDATA[ToA Ranging and Layer Thickness Computation in Nonhomogeneous Media]]>5527427523682<![CDATA[Context-Adaptive Pansharpening Based on Image Segmentation]]>55275376618370<![CDATA[A Model of Radar Backscatter of Rain-Generated Stalks on the Ocean Surface]]>5527677763203<![CDATA[A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis]]>55277778620492<![CDATA[Imaging for High-Resolution Wide-Swath Spaceborne SAR Using Cubic Filtering and NUFFT Based on Circular Orbit Approximation]]>55278780013719<![CDATA[Modeling Elastic Wave Propagation Using $K$ -Space Operator-Based Temporal High-Order Staggered-Grid Finite-Difference Method]]> -space approach. A split QWE (SQWE) is further developed, and numerical simulation of SQWE results in separated P (compressional)-wave and S (shear)-wave. Theoretical computational cost analysis verifies that the numerical simulation of QWE using the temporal fourthand sixth-order SGFD schemes is more efficient than the numerical simulation of the traditional stress–velocity wave equation using the traditional temporal second-order SGFD scheme in 2-D. In 3-D, the temporal fourth-order SGFD scheme still runs faster than the traditional temporal second-order scheme; however, the temporal sixth-order scheme is more efficient only when a longer stencil length than 12 is adopted. Numerical examples confirm the correctness of the developed elastic wave modeling schemes.]]>5528018159808<![CDATA[Vicarious Cold Calibration for Conical Scanning Microwave Imagers]]>5528168279623<![CDATA[A Novel Probabilistic Method to Model the Uncertainty of Tidal Prediction]]>5528288331346<![CDATA[Ultrawideband FMCW Radar for Airborne Measurements of Snow Over Sea Ice and Land]]>5528348434926<![CDATA[Hyperspectral Image Classification Using Deep Pixel-Pair Features]]>5528448533546<![CDATA[Millimeter-Wave Radar Sensor for Snow Height Measurements]]>5528548614019<![CDATA[On the Sampling Strategy for Evaluation of Spectral-Spatial Methods in Hyperspectral Image Classification]]>55286288016621<![CDATA[Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks]]>55288189310042<![CDATA[Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection]]>5528949063089<![CDATA[Evaluation of GRACE Mascon Gravity Solution in Relation to Interannual Oceanic Water Mass Variations]]>www.ecco-group.org)] steric sea level. The mascon solution was consistently more accurate than its spherical harmonic counterpart across large spatial and temporal scales, due mainly to the inherent smoothing from the mascon cells. Comparison of GRACE with both GECCO2 + altimetry and Argo + altimetry mass estimates revealed an offset in phase with regard to the annual cycle, but yielded an rmse of only 5.6 mm in the interannual signal after phase correction. This paper furthers evidence of an accelerated water cycle at a rate of 1.5% ± 1.1% at low latitudes, and provides a means of validation for oceanic freshwater budget studies based on salinity measurements.]]>55290791410287<![CDATA[Pulse Compression Waveform and Filter Optimization for Spaceborne Cloud and Precipitation Radar]]>5529159313677<![CDATA[Estimation of Doppler Profile Using Multiparameter Cost Function Method]]>Doppler frequency shift and the time of flight of the echo signals from each beam are measured. These data are analyzed to get the radial velocities of atmospheric targets at all the ranges. The data must be processed from at least three noncoplanar beam directions together to estimate 3-D wind profile. The signal-to-noise ratio is very low for the higher range target echoes. Also, echoes are often contaminated by nonatmospheric signals like clutter, radio frequency interference, and so on. Determining accurate wind profile, especially at higher heights, becomes a challenging task due to these factors. This paper presents a novel algorithm for the estimation of radial velocity profile by processing the set of Doppler power spectra of all range bins. The algorithm identifies prospective atmospheric echoes components. Then it forms velocity profile trails connecting five range bins. The program rejects the profile trails that show velocity change higher than a predefined limit. The remaining trails are evaluated using a specially designed multiparameter cost function (MPCF). The trails with maximum cost are selected and then connected to construct the complete Doppler profile or the radial velocity profile. The key innovation in this method is an improvised function that is created by weighted addition of two terms. The first term is proportional to the signal power, and the second term is a nonlinear function of differential wind shear. This algorithm has been tried on multiple sets of the Indian mesosphere–stratosphere–troposphere radar data and the lo-
er atmospheric WP radar data. The performance of this method is compared with other methods, namely, the adaptive moment estimation method and fuzzy logic approach. It has been observed that the new method shows excellent consistency in extracting the Doppler profile from the power spectral data. This method requires at least 30% lesser computations compared with other methods. The results of the MPCF method showed a very good match with the data obtained from concurrently operated Global Positioning System sonde, an independent wind profiling method. This paper also shows the results on the performance improvement using power spectra from the symmetrical beams. This algorithm shows a great promise as a tool for automatic Doppler profile tracing.]]>5529329422399<![CDATA[Modeling Sea Ice Surface Emissivity at Microwave Frequencies: Impact of the Surface Assumptions and Potential Use for Sea Ice Extent and Type Classification]]>5529439616551<![CDATA[Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm]]>5529629774344<![CDATA[DEM Estimation for LASAR Based on Variational Model]]>5529789953164<![CDATA[Road Curb Extraction From Mobile LiDAR Point Clouds]]>55299610099601<![CDATA[An Efficient Undersampled High-Resolution Radon Transform for Exploration Seismic Data Processing]]>5521010102410730<![CDATA[A Comprehensive Evaluation of Microwave Emissivity and Brightness Temperature Sensitivities to Soil Parameters Using Qualitative and Quantitative Sensitivity Analyses]]>552102510384233<![CDATA[Severe Thunderstorm Detection by Visual Learning Using Satellite Images]]>5521039105211542<![CDATA[On the Accuracy of Topographic Residuals Retrieved by MTInSAR]]>552105310656294<![CDATA[Multitemporal Backscattering Logistic Analysis for Intertidal Bathymetry]]>552106610739809<![CDATA[Building Occlusion Detection From Ghost Images]]>5521074108416664<![CDATA[Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification]]>5521085110521277<![CDATA[Accuracy of Nearshore Bathymetry Inverted From ${X}$ -Band Radar and Optical Video Data]]>$^{2}$ ) nearshore regions over many years. With recorded wave frequency $Omega $ and wavenumber $k$ (and hence wave phase speed $c = Omega /k$ ), bed elevation $z_{b}$ can be derived using a model that relates $Omega $ and $k$ to water depth. However, the accuracy of $z_{b}$ as a function of the sensor and the method of $Omega -k$ retrieval is not well known, especially not under low-period waves. Here, we assess the accuracy of $z_{b}$ , based on two sensors with their own method of phase speed retrieval, in a dynamic, kilometer-scale environment (Sand Engine, The Netherlands). Bias in $z_{b}$ is systematic. A fast Fourier transform (FFT) method on ${X}$ -band radar imagery produced $z_{b}$ too shallow by 1.0 m for $-15~text {m} leq z_{b} leq -9$ m, and too deep by 2.3 m for $z_{b}geq -6$ m. A cross-spectral method on optical video imagery produced $z_{b}$ too shallow by 0.59 m for $-10~text {m} leq z_{b} leq -5$ m, and too deep by 0.92 m for $z_{b}geq -1$ m. Intermediate depths had negligible bias, −0.02 m for the radar-FFT approach and −0.01 m for the video-CS approach. The collapse of the FFT method in shallow water may be explained by the inhomogeneity of the wave field in the 960 m $times960$ m analysis windows. A shoreward limit of the FFT method is proposed that depends on $z_{b}$ in the analysis windows.]]>552110611163784<![CDATA[Adaptive Nonzero-Mean Gaussian Detection]]>552111711243720<![CDATA[Uniaxial Complex Relative Permittivity Tensor Measurement of Rocks From 40 Hz to 4.5 GHz]]> ) or open (infinite ohms) to obtain dispersion curves in both the axial and radial directions. The theoretical basis of each of the above systems is described. Two reservoir rocks are tested and their results are reported. In conclusion, the added value this laboratory capability presents will yield a higher quality of borehole data and a more quantitatively accurate petrophysical interpretation.]]>552112511393153<![CDATA[On the Construction of CFAR Decision Rules via Transformations]]>a priori knowledge of the Pareto scale parameter. It is shown here that this shortcoming can be rectified by application of a complete sufficient statistic to the transformed detector. Consequently, new decision rules are derived and it is shown that they not only achieve the CFAR property but in some instances can improve the performance of the decision rules from which they are derived.]]>552114011461909<![CDATA[Characterization of Long-Term Stability of Suomi NPP Cross-Track Infrared Sounder Spectral Calibration]]>552114711595470<![CDATA[Characterizing Vegetation Canopy Structure Using Airborne Remote Sensing Data]]>5521160117819016<![CDATA[Through-Casing Hydraulic Fracture Evaluation by Induction Logging I: An Efficient EM Solver for Fracture Detection]]>552117911882323<![CDATA[Through-Casing Hydraulic Fracture Evaluation by Induction Logging II: The Inversion Algorithm and Experimental Validations]]>552118911984117<![CDATA[Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation]]>552119912091916<![CDATA[A Framework of Mixed Sparse Representations for Remote Sensing Images]]>a posteriori when solving ill-conditioned problems such as classification and super resolution (SR), respectively. The experimental results show that not only the new framework of MSR can improve classification accuracy but also it can construct a much better high-resolution image than other common SR methods. The proposed framework MSR is a competitive candidate for solving other remote sensing images-related ill-conditioned problems.]]>552121012214326<![CDATA[Corrections to “A Combined Rotational Raman-Rayleigh Lidar for Atmospheric Temperature Measurements Over 5–80 km With Self-Calibration”]]>[1], there are two errors on page 7058. In addition, the support information has been updated to include an additional funding source. The complete funding statement should be:]]>5521222122218<![CDATA[Introducing IEEE Collabratec]]>552122312232200<![CDATA[IEEE Transactions on Geoscience and Remote Sensing information for authors]]>552C3C388<![CDATA[IEEE Transactions on Geoscience and Remote Sensing institutional listings]]>552C4C4321