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This paper deals with the compensation of phase miscalibration in the general context of tomographic synthetic aperture radar image focusing. Phase errors are typically independent of one acquisition to the other, thus leading to a spreading and defocusing in the multidimensional (3-D, 4-D, and 5-D) imaging space. Coping with this problem in presence of volumetric scattering is generally a complex issue. In this paper, we consider the approach for phase calibration characterized by the advantage, with respect to classical phase calibration algorithms, of not requiring either the identification of a reference target or specific assumptions about the unknown phase function, or a priori information about the terrain topography. The novelty of the proposed phase miscalibration estimation and compensation method is related to its ability to avoid unwanted and uncontrollable vertical shifts in the focused image. The estimation of the calibration phase is performed by optimizing the contrast or the entropy of the vertical profile with the constraint of a zero phase derivative. Such a constraint preserves the output height distribution. Experimental results of simulated and real data are included to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques have been generally more popular, despite the fact that it is difficult to collect labeled samples in real scenarios. In particular, deep neural networks, such as convolutional neural networks (CNNs), have recently shown a great potential to yield high performance in the hyperspectral image classification. However, these techniques require sufficient labeled samples in order to perform properly and generalize well. Obtaining labeled data is expensive and time consuming, and the high dimensionality of hyperspectral data makes it difficult to design classifiers based on limited samples (for instance, CNNs overfit quickly with small training sets). Active learning (AL) can deal with this problem by training the model with a small set of labeled samples that is reinforced by the acquisition of new unlabeled samples. In this paper, we develop a new AL-guided classification model that exploits both the spectral information and the spatial-contextual information in the hyperspectral data. The proposed model makes use of recently developed Bayesian CNNs. Our newly developed technique provides robust classification results when compared with other state-of-the-art techniques for hyperspectral image classification. [ABSTRACT FROM AUTHOR]

One-minute rain rate exceeded for 0.01% of an average year (known as R0.01) is an important parameter, which is required in rain fade models for planning both satellite and terrestrial links, especially in the microwave and millimeter-wave bands. This paper presents the development of a new global model for estimating high-resolution R0.01. The model is developed using the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), ERA-Interim reanalyzed total precipitable water, and the cumulative distribution functions of the measured R0.01 via genetic algorithm. The newly proposed model performs better than the International Telecommunication Union Radio Sector (ITU-R) recommendation and widely used ITU-R P.837-6 model, particularly over the tropical regions, where ITU-R P.837-6 strongly underestimates R0.01. Overall, when compared to ITU-R P.837-6, the estimated R0.01 by the proposed model is improved by ~15%. Furthermore, the proposed model can provide R0.01 with a much higher spatial resolution (~5 km) as compared to ITU-R P.837-6 (~110 km). Another key advantage of the proposed model over ITU-R P.837-6 is that the inputs required for this model are readily available from meteorological database as well as from space-based observing system. This paper also investigated the long-term (1981–2016) trends in R0.01. The analysis revealed significant increasing trends in R0.01 over most parts of the globe, India and northern part of Southern America exhibited the strongest ($\sim 0.5\,\,\text {mm}\cdot \text {h}^{-1}\cdot $ year−1) increasing trends. It is anticipated that R0.01 obtained with the newly proposed model will have large implications in rain attenuation modeling for planning both terrestrial and earth–satellite microwave links. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they have been merely used in hyperspectral unmixing as a straightforward data decomposition process. This limits their actual capabilities to provide semantic representations of the spectral data. The proposed model, called dual-depth sparse probabilistic latent semantic analysis (DEpLSA), makes use of two different levels of topics to exploit the semantic patterns extracted from the initial spectral space in order to relieve the ill-posed nature of the unmixing problem. In other words, DEpLSA defines a first level of deep topics to capture the semantic representations of the spectra, and a second level of restricted topics to estimate endmembers and abundances over this semantic space. An experimental comparison in conducted using the two standard topic models and the seven state-of-the-art unmixing methods available in the literature. Our experiments, conducted using four different hyperspectral images, reveal that the proposed approach is able to provide competitive advantages over available unmixing approaches. [ABSTRACT FROM AUTHOR]

This paper assesses the performance of water vapor measurements by Sondeur Atmosphérique du Profild’ Humidité Intertropicale par Radiométrie (SAPHIR), microwave limb sounder (MLS), and the global reanalysis water vapor data [Modern Era Retrospective analysis for Research and Applications (MERRA) and European Center for Medium-Range Weather Forecasts reanalysis (ERA) interim] by comparing with water vapor-measured in situ by cryogenic frost-point hygrometer (CFH) at two tropical stations, Trivandrum (8.5°N, 76.9°E) and Hyderabad (17.47°N, 78.58°E). The iMet-1 radiosonde though overestimates frost-point temperature (3%–5%) in the 4–10-km altitude region with respect to CFH, it agrees well with CFH observations in the upper troposphere and the lower troposphere with a mean difference <2% and ~1%, respectively. SAPHIR–CFH intercomparison is done for the troposphere region, and MLS, MERRA, and ERA interim are compared with CFH in the lower stratosphere region. SAPHIR and CFH comparison shows a reasonable agreement between both the datasets with a relative humidity difference of about 15% in the lower and middle troposphere and a dry bias of ~40% in the upper troposphere. Intercomparison between CFH and MLS water vapor mixing ratios (WVMRs) shows a small wet bias (−10% to −20%) for the MLS in the lower stratospheric region between 100 and 50 hPa and a small dry bias (<10%) above that region. This paper shows that the MLS always underestimates when the CFH WVMR is greater than 6 ppmv and overestimates when the WVMR is less than 2 ppmv. The intercomparison of CFH with MERRA and ERA interim specific humidity shows results similar to that with MLS. [ABSTRACT FROM AUTHOR]

This paper describes the improvements in the simulation of a heavy rainfall event due to the assimilation of surface wind observations from the Oceansat-2 scatterometer using ensemble Kalman filter (EnKF) technique. A heavy rainfall event over the southern peninsular region of India during the northeast Indian monsoon season is investigated in this paper using the Advanced Research Weather Research and Forecasting model. A control (CTRL) run where no surface wind observations are assimilated, as well as a 3-D variational (3DVar) run and an EnKF run wherein surface wind observations are assimilated using the 3DVar and EnKF techniques, is performed. Results indicate that the EnKF assimilation run simulates various meteorological fields, including precipitation fields during the rainfall event, better than the CTRL and the 3DVar runs. Qualitative and quantitative comparisons with Tropical Rainfall Measurement Mission precipitation observations indicate that the rainfall simulation shows improvement due to EnKF assimilation as compared with the other two model runs. Vertical profiles of area-averaged and time-averaged relative vorticities and temperature anomalies around the low-pressure system are also better reproduced in the EnKF experiment. Considering the importance of accurate real time simulations of heavy rainfall events associated with the Indian monsoon season, this paper provides encouraging results on the utility of EnKF technique as applied over the Indian region. [ABSTRACT FROM PUBLISHER]

Detection of small targets, more specifically cars, in aerial images of urban scenes, has various applications in several domains, such as surveillance, military, remote sensing, and others. This is a tremendously challenging problem, mainly because of the significant interclass similarity among objects in urban environments, e.g., cars and certain types of nontarget objects, such as buildings’ roofs and windows. These nontarget objects often possess very similar visual appearance to that of cars making it hard to separate the car and the noncar classes. Accordingly, most past works experienced low precision rates at high recall rates. In this paper, a novel framework is introduced that achieves a higher precision rate at a given recall than the state of the art. The proposed framework adopts a sliding-window approach and it consists of four stages, namely, window evaluation, extraction and encoding of features, classification, and postprocessing. This paper introduces a new way to derive descriptors that encode the local distributions of gradients, colors, and texture. Image descriptors characterize the aforementioned cues using adaptive cell distributions, wherein the distribution of cells within a detection window is a function of its dominant orientation, and hence, neither the rotation of the patch under examination nor the computation of descriptors at different orientations is required. The performance of the proposed framework has been evaluated on the challenging Vaihingen and Overhead Imagery Research data sets. Results demonstrate the superiority of the proposed framework to the state of the art. [ABSTRACT FROM PUBLISHER]

This paper critically analyzes several incoherent model-based decomposition methods for assessing the effect of deorientation in characterization of various land covers. It has been found that even after performing decomposition, ambiguity still occurs in scattering response from various land covers, such as urban and vegetation. Researchers introduced the concept of deorientation to remove this ambiguity. Therefore, in this paper, a critical analysis has been carried out using seven different three- and four-component decomposition methods with and without deorientation and two Eigen decomposition-based methods to investigate the scattering response on various land covers, such as urban, vegetation, bare soil, and water. The comprehensive evaluation of decomposition and deorientation effect has been performed by both visual and quantitative analyses. Two types of quantitative analysis have been performed; first, by observing percentage of scattering power and second, by analyzing the variation in the number of pixels in different land covers for each scattering contribution. The analysis shows that deorientation increases not only the power but also the number of pixels for surface and double bounce scattering. The number of pixels representing volume scattering remain almost the same for all the methods with or without deorientation, whereas volume scattering power reduces after deorientation. Eigen decomposition-based methods are observed to solve the problem of overestimation of volume scattering power. [ABSTRACT FROM PUBLISHER]

Sea ice concentration (SIC) is an important sea ice parameter for sea ice navigation, environmental research, and weather and ice forecasting. We have developed and tested a method for estimation of the Baltic Sea SIC using SENTINEL-1 synthetic aperture radar (SAR) and Advanced Microwave Scanning Radiometer 2 passive microwave radiometer (MWR) data. Here, we present the method and results for January 2016. Ice concentration grids of Finnish Meteorological Institute daily ice charts have been used as reference data in this paper. We present a comparison of four SIC estimation methods with our reference data. In addition to the combined SAR/MWR SIC estimation method, we also compare SIC estimates produced using SAR alone and two MWR-based methods. The main target of this paper was to develop and test a high-resolution SIC estimation method suitable for operational use. [ABSTRACT FROM PUBLISHER]

This paper describes the preflight spectral calibration methods and results for the Orbiting Carbon Observatory 2 (OCO-2), following the approach developed for the first OCO. The instrument line shape (ILS) function and dispersion parameters were determined through laser-based spectroscopic measurements, and then further optimized by comparing solar spectra recorded simultaneously on the ground by the OCO-2 flight instrument and a collocated high-resolution Fourier transform spectrometer (FTS). The resulting ILS profiles and dispersion parameters, when applied to the FTS solar data, showed agreement between the spectra recorded by the spectrometers and FTS to approximately 0.2% RMS, satisfying the preflight spectral calibration accuracy requirement of <0.25% RMS. Specific changes to the OCO-2 instrument and calibration process, compared to the original OCO, include stray-light protection; improved laser setup; increased spectral sampling; enhanced data screening, and incremental improvements in the ILS, dispersion, and FTS optimization analyses. [ABSTRACT FROM PUBLISHER]

The availability of accurate trajectory information is paramount for the processing and exploitation of synthetic aperture radar (SAR) data. Considering the particular case of spaceborne SARs designed for repeat-pass interferometric applications, errors in the trajectory translate into phase artifacts that affect the interferometric performance. In this paper, we propose a model-based procedure to calibrate the trajectories of spaceborne SAR systems by the multisquint (MS) phase. The technique allows to estimate the along and the derivative of across track geometric errors. The geometric model of the InSAR phase is derived as a function of positioning errors and the MS phase model as derivative of the InSAR phase geometric model, with respect to the squint angle. We perform a sensitivity analysis of the model in order to define which geometric errors can be estimated by the MS phase, justifying the assumption that the MS phase is very poorly affected by the atmospheric phase screen. We particularly concentrate on the TOPSAR acquisition mode, where the phase is very sensitive to geometric errors. We start from the classical two-image case and then consider the extension to the multibaseline case. Experimental results obtained by processing of interferometric pairs acquired by the Sentinel-1A sensor are reported. [ABSTRACT FROM PUBLISHER]

This paper describes the Naval Research Laboratory Multichannel Synthetic Aperture Radar (NRL MSAR) and presents initial results from the first field deployment of this system. The NRL MSAR is an airborne test bed designed to investigate remote sensing and surveillance applications that exploit multiple along-track phase centers, particularly applications that require measurement of scene motion. The system operates at X-band and supports 32 along-track phase centers through the use of two transmit horns and 16 receive antennas. As illustrated in this paper, SAR images generated with these phase centers can be coherently combined to directly measure scene motion using the velocity SAR algorithm, and these measurements can then be used to correct the image distortion that the motion causes. In September 2014, this unique radar was deployed for the first time on an airborne platform. This paper presents a description of the system and results from its inaugural deployment, including the correction of distorted maritime signatures. These data were collected over an ocean inlet and contain a variety of moving backscatter sources, including automobiles, ships, shoaling ocean waves, and tidal currents. [ABSTRACT FROM PUBLISHER]

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal synthetic aperture radar (SAR) data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1 $\beta$ products are particularly oriented toward nonexpert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of this paper is the design of a suitable RGB representation of data aiming to enhance the information content of the time-series. The physical rationale of the products is presented through examples, in which we show their robustness with respect to sensor, acquisition mode, and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new European Space Agency sensor. Finally, we propose two applications based on the use of Kohonen's self-organizing maps dealing with classification problems. [ABSTRACT FROM PUBLISHER]

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Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that, at the present, can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators [e.g., attribute filters (AFs)] have proven their effectiveness in processing very high resolution images. AFs are based on attributes which can be efficiently implemented on tree-based image representations. In this paper, we considered the definition of \min, \max, \textdirect, and \textsubtractive filter rules for the computation of AFs over the tree-of-shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier, and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas. [ABSTRACT FROM AUTHOR]

This paper investigates the bistatic scattering from randomly corrugated surfaces with irregular grooves. For a given incident wave, the surface fields and the scattered field were computed by the method of moments in which the rooftop basis function was used to account for fast phase changes due to steep surface slopes. The total scattered field is decomposed into coherent and incoherent components to analyze their respective contributions. We found that, for randomly corrugated surfaces with irregular ridges or grooves, the coherent scattering is profound at several scattering angles with strong main lobes, whose beamwidths are strongly correlated with the ridge density. The numerical simulation has shown that the lobe angular shift away from the specular direction is quasi-linearly dependent on the ridge density, suggesting that the coherent scattering pattern substantially contains the surface geometric information. We expect this paper to offer deeper understanding of coherent imaging of rough surface and to help in designing a novel imaging system. [ABSTRACT FROM AUTHOR]

High-resolution thermal infrared (TIR) remote sensing has a wide range of applications. In this paper, we describe the different applications and requirements identified in a literature review and during a consultation meeting with researcher experts in different fields. As a result, more than 30 applications were identified within three different fields: 1) land and solid Earth; 2) health and hazards; and 3) security and surveillance. A complete set of requirements (spatial, temporal, and radiometric resolution, algorithms used, and supporting data, among others) for each application is also provided. The results presented in this paper provide useful information to enhance the importance of high-resolution TIR data for civil applications and may serve as a reference document for future TIR mission concepts. [ABSTRACT FROM AUTHOR]

One efficient method to detect slow targets with low signal-to-clutter-plus-noise ratios is space–time adaptive processing (STAP). To apply STAP to maritime radar systems, the multichannel properties of sea clutter have to be known. This paper investigates these characteristics for airborne microwave radar systems to detect vessels in the open sea. It is shown that the motion of sea echoes and different sea scattering types cause different multichannel properties of sea clutter compared with land clutter. This paper provides a mathematical basis to describe multichannel sea clutter and predict STAP performance over water. To confirm the derived properties, several real multichannel sea data sets for different swell directions and sea states were acquired with the airborne radar system Phased Array Multifunctional Imaging Radar (PAMIR), and simulations were conducted. This paper demonstrates the reproducibility of real sea clutter multichannel characteristics for different sea conditions with the help of calculations and simulations, which are proposed in this analysis. [ABSTRACT FROM PUBLISHER]

Sampling plays a critical role in remote sensing and signal analysis. In conventional sampling theory, the signal is sampled at a uniform rate at a minimum of twice the signal bandwidth. Sampling with an aperture function requires a fixed-aperture function, which can be removed by deconvolution after signal reconstruction. However, in some cases, the signal samples are available only at irregular positions, and different samples use different aperture functions. In this paper, the theory of finite-length signal reconstruction with irregular samples and variable apertures in one and two dimensions is considered. In the 1-D case, a band-limited discrete signal can be exactly reconstructed from a finite number of arbitrarily spaced samples with few restrictions on the aperture functions. Exact reconstruction in the 2-D case requires the sampling matrix be invertable, and is not always possible. Variable aperture functions, while complicating the process, can enable reconstruction for a broader range of sample locations. Practical issues are discussed, and numerical examples are provided. Variable aperture reconstruction has application in a variety of remote sensing problems. In this paper, reconstruction from 2-D irregular sampling with variable apertures is illustrated using Special Sensor Microwave/Imager radiometer observations. [ABSTRACT FROM PUBLISHER]

For any quantitative applications that use reflectivity and/or differential reflectivity, radar observations need to be compensated for attenuation effects due to precipitation. This paper presents a robust attenuation correction system (ACS) for dual-polarization radars correcting the reflectivity factor as well as differential reflectivity profiles. The major advantage of the algorithm described in this paper is that the procedures are immune to the bias effect of reflectivity and differential reflectivity. In addition, this method is not very sensitive to the variation of temperature. The proposed ACS has been evaluated with X-band radar observations simulated from drop size distribution derived from high-resolution S-band measurements observed by the CSU-CHILL radar. The evaluation of the proposed retrieval algorithm shows that the retrieved reflectivity and differential reflectivity provide an improvement over the conventional self-consistent attenuation correction technique with the differential phase constraint. [ABSTRACT FROM PUBLISHER]

Canopy height models (CHMs) were utilized to detect treetops and estimate individual-tree parameters. The treetop detection based on CHMs was affected by surface topography and crown characteristics. However, their effects have not been well studied. Therefore, this paper aimed at assessing the impacts of aforementioned factors to facilitate treetop identification from LiDAR-derived CHMs. To fulfill this objective, we first extended and improved the previous models for cases with various terrains. Then, a new theoretical model was developed to quantify treetop displacements for ellipsoidal tree crowns. Finally, we further analyzed the treetop displacements due to terrain slope, crown radius, crown shape, and offset distance to the slope surface. Our analysis indicates that the vertical displacement increases exponentially with terrain slope; thus, the effect of terrain slope must be considered over extremely steep areas; larger errors are observed for trees with a large crown radius; the treetop displacements are highly correlated with crown shape, the effect of topographic normalization can be neglected for conical crowns with a large crown angle, and the elliptical crown shape can reduce the treetop detection errors; and treetop displacements increases with offset distance to the slope surface in , while opposite results are observed in. In addition, the results also demonstrate that the effect of slope-distorted CHMs may be quite different for different types of tree crowns and terrains. Overall, this paper makes a significant contribution to the development of theoretical models for quantifying treetop displacements. Furthermore, our findings provide a theoretical basis and guidance for better identifying treetops from LiDAR-derived CHMs. [ABSTRACT FROM AUTHOR]

This paper proposes a phase-derived velocity measurement (PDVM) method for high-speed targets based on wideband direct sampling linear frequency modulated radar. First, a high-speed target echo model considering intrapulse Doppler modulation is developed. Then, a PDVM model considering acceleration is established. The key to realizing PDVM is resolving phase ambiguity. Under low signal-to-noise ratio (SNR) conditions, a joint processing method combining acceleration information and multiframe data to solve phase ambiguity is proposed, which can significantly reduce the SNR requirement for PDVM. In this paper, the small-amplitude micromotion measurement capability of the proposed method is verified by simulation. Moreover, the measured data of a Ku-band ground-based radar are used to verify the applicability of the PDVM method under low SNR conditions and its feasibility to be applied to complex multi-scattering point targets. Both the simulation and experimental results show that the proposed method is suitable for high-speed targets with radial motion, including acceleration and jerk, and that the PDVM precision can reach the order of magnitude of centimeters per second or millimeters per second. [ABSTRACT FROM AUTHOR]

Monitoring the sea ice cover is important for both climate studies and ice operations, such as shipping. It is challenging to validate even basic essential variables, such as the sea ice extent, due to a lack of appropriate validation data. Instead of focusing on validation, this paper looks at the use of categorical triple collocation (CTC) for the task of quantitatively comparing three colocated data sets. CTC has been developed and used in earlier studies to rank binary data sets. In this paper, we extend earlier studies and bring in recent results from the binary classification community to estimate the class imbalance (the relative proportion of each class, ice or water). We then use this class imbalance to obtain quantitative estimates of the proportion correct of ice (sensitivity) and the proportion correct of water (specificity). The methodology is first tested using toy data, after which three data sets from the Gulf of Saint Lawrence, on the east coast of Canada, are used. These data sets are from an ice–ocean model, a passive microwave sea ice concentration retrieval, and a sea ice concentration retrieval from synthetic aperture radar (SAR). By looking at both the sensitivity and the specificity, it is found that the passive microwave data have difficulty in recognizing ice during freeze-up, but they perform well at obtaining the correct water observations. This distinction cannot be made by ranking the data sets. The CTC method is compared with, and found to be complementary to, a validation using ice/water states from the interactive multisensor snow and ice mapping system. [ABSTRACT FROM AUTHOR]

Advances in remote sensing hardware have led to a significantly increased capability for high-quality data acquisition, which allows the collection of remotely sensed images with very high spatial, spectral, and radiometric resolution. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and analyzed. An important approach to deal with massive volumes of information is data compression, related to how data are compressed before their storage or transmission. For instance, hyperspectral images (HSIs) are characterized by hundreds of spectral bands. In this sense, high-performance computing (HPC) and high-throughput computing (HTC) offer interesting alternatives. Particularly, distributed solutions based on cloud computing can manage and store huge amounts of data in fault-tolerant environments, by interconnecting distributed computing nodes so that no specialized hardware is needed. This strategy greatly reduces the processing costs, making the processing of high volumes of remotely sensed data a natural and even cheap solution. In this paper, we present a new cloud-based technique for spectral analysis and compression of HSIs. Specifically, we develop a cloud implementation of a popular deep neural network for non-linear data compression, known as autoencoder (AE). Apache Spark serves as the backbone of our cloud computing environment by connecting the available processing nodes using a master–slave architecture. Our newly developed approach has been tested using two widely available HSI data sets. Experimental results indicate that cloud computing architectures offer an adequate solution for managing big remotely sensed data sets. [ABSTRACT FROM AUTHOR]

Compression of hyperspectral images onboard of spacecrafts is a tradeoff between the limited computational resources and the ever-growing spatial and spectral resolution of the optical instruments. As such, it requires low-complexity algorithms with good rate-distortion performance and high throughput. In recent years, the Consultative Committee for Space Data Systems (CCSDS) has focused on lossless and near-lossless compression approaches based on predictive coding, resulting in the recently published CCSDS 123.0-B-2 recommended standard. While the in-loop reconstruction of quantized prediction residuals provides excellent rate-distortion performance for the near-lossless operating mode, it significantly constrains the achievable throughput due to data dependencies. In this paper, we study the performance of a faster method based on the prequantization of the image followed by a lossless predictive compressor. While this is well known to be suboptimal, one can exploit powerful signal models to reconstruct the image at the ground segment, recovering part of the suboptimality. In particular, we show that convolutional neural networks can be used for this task and that they can recover the whole SNR drop incurred at a bit rate of 2 bits per pixel. [ABSTRACT FROM AUTHOR]

This paper investigates new approaches to estimating the motion of the dynamic ocean surface using a multi-channel synthetic aperture radar (MSAR) with $M$ phase centers arranged in an along-track configuration. The objective of this paper is to determine the processing methods that produce the finest velocity resolution, an issue that arises due to the finite coherence time of radar backscatter produced by the sea surface. The investigation is carried out both theoretically, using synthesized data produced from a modeled MSAR covariance matrix, as well as experimentally, using images collected with a 16-channel system. Three processing methods are considered: linear regression along with a multi-baseline phase progression, estimation of the velocity centroid, and coherent averaging of the shortest baseline interferograms. Both the theoretical and experimental results indicate that simple averaging of the shortest-baseline interferograms often produces the best velocity precision. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) in the time domain of ground-penetrating radar (GPR) data involves a vast number of calculations; thus, it requires a large amount of memory and is difficult to calculate on a personal computer (PC). In this paper, GPR data are analyzed with multiscale FWI using two parameters (permittivity and conductivity) based on total variation (TV) regularization, which is implemented on a PC using a graphics processing unit (GPU) parallel acceleration strategy. The inverse problem is considered to be a nonlinear optimization problem and is solved with limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) process, which is a quasi-Newton method. The gradient of the objective function is calculated using the adjoint-state method, and the finite-difference method is required to solve the forward problem many times. A multiscale serial inversion strategy is applied to optimize the inversion algorithm and to decompose the inversion problem into 2–3 frequency bands to search in the direction of the global minimum point instead of local minimums. Taking the complex model as an example, experiments are carried out to assess the parameter adjustment factor and regularization parameter. The appropriate parameter adjustment factor and regularization parameter can effectively guarantee the convergence speed and stability of dual-parameter inversion method and improve the accuracy of GPR data inversion. Finally, FWI of the noise-free and 25-dB signal-to-noise ratio (SNR) noise data of the overthrust model is performed. The results show that the multiscale and dual-parameter inversion method proposed in this paper can provide reliable constraints, has better adaptability to noisy data, and can reliably and accurately reconstruct the dielectric properties distribution of the subsurface. [ABSTRACT FROM AUTHOR]

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the NOAA-20 (previously JPSS-1) spacecraft has successfully operated since its launch in November, 2017. Similar to the first VIIRS instrument on the Suomi-National Polar-orbiting Partnership (SNPP) spacecraft, the data are collected in 22 spectral bands that are calibrated by a set of onboard calibrators. This paper provides an overview of the NOAA-20 VIIRS on-orbit operation and calibration, with a particular focus on the thermal emissive bands (TEBs). The results presented in this paper include the on-orbit changes in the TEB spectral band responses, detector noise characterization, and key calibration parameters, such as the nonlinear coefficients derived from the blackbody warm-up cool-down cycles. Other issues, such as the early mission long-wave infrared (LWIR) response degradation due to icing on the dewar window, and their impact on sensor calibration are also discussed. Since launch, the VIIRS instrument temperature has been stable to within ±0.8 K and the cold focal plane temperatures are well controlled with variations less than 40 mK. With the exception of the early degradation observed in the LWIR bands, the TEB gains have been stable to within 0.04% (except I5 at 0.07%). Based on the current performance, VIIRS is expected to meet its calibration requirements throughout its design lifetime. [ABSTRACT FROM AUTHOR]

Compared with frequency-domain algorithms, time-domain algorithms (TDAs) can achieve image focusing under the conditions of arbitrary trajectories and large integration angles. However, the interpolations in both range and bearing angle directions are required in the coordinate transformation of fast TDAs, which inevitably increases the computational load and introduces interpolation errors. In this paper, a fast time-domain imaging and corresponding autofocus method based on the hybrid coordinate (HC) system is proposed. First, the interpolation operation is optimized in the process of fast TDAs. It transforms the 2-D interpolation into 1-D interpolation only in the bearing angle direction, which improves the execution efficiency and reduces the interpolation error. Next, a 3-D trajectory deviation estimation method based on Gauss–Newton iteration is investigated for the motion compensation in the HC system. By iterative optimization, the 3-D motion errors during the flight are estimated accurately, and the space-variant phase error is compensated precisely. This method has good robustness and universality. Simulation results and real data processing demonstrate the effectiveness and the practicability of the method presented in this paper. [ABSTRACT FROM AUTHOR]

This paper investigates at the example of bathymetry how much an application can profit from comprehensive characterizations required for an improved calibration of data from a state-of-the-art commercial hyperspectral sensor. A NEO HySpex VNIR-1600 sensor is used for this paper, and the improvements are based on measurements of sensor properties not covered by the manufacturer, in particular, detector nonlinearity and stray light. This additional knowledge about the instrument is used to implement corrections for nonlinearity, stray light, spectral smile distortion and nonuniform spectral bandwidth and to base the radiometric calibration on a SI-traceable radiance standard. Bathymetry is retrieved from a data take from the lake Starnberg using WASI-2D. The results using the original and improved calibration procedures are compared with ground reference data, with an emphasis on the effect of stray-light correction. For our instrument, stray-light biases the detector response from 416–500 nm up to 8% and from 700–760 nm up to 5%. Stray-light-induced errors affect bathymetry mainly in water deeper than Secchi depth, whereas in shallower water, the dominant error source is the calibration accuracy of the light source used for radiometric calibration. Stray-light correction reduced the systematic error of water depth by 19% from Secchi depth to three times Secchi depth, whereas the relative standard deviation remained stable at 5%. [ABSTRACT FROM PUBLISHER]

This paper reports the first use of a 3-D radar imaging for observing E-region field-aligned plasma irregularities (FAIs) in the midlatitude ionosphere. Five carrier frequencies equally spaced between 46.25 and 46.75 MHz were transmitted alternately with consecutive radar pulses, and 20 receivers were employed for receiving the radar echoes. The experiments were carried out using the middle and upper atmosphere radar (34.85°N, 136.10°E), with the radar beams steered to the geographic north and 6.6° north by east, at the zenith angles of 51.0° and 51.5°, respectively. The 2-D imaging with the echoes collected by the 19 of the 20 receivers revealed that in zonal dimension, the off-beam direction of arrival (DOA) of the echo region changed alternately between positive and negative values, which can be attributed to the highly localized FAIs drifting zonally through the radar beam. On the other hand, the off-beam DOA in meridional dimension was negative and positive, respectively, at higher and lower range locations, which were supposed to be due to the meridional drift component of FAIs. A combination of 5 frequencies and 19 receivers achieved a 3-D imaging of the FAI structures, illustrating small-scale FAI striations and bubblelike plasma structures in the radar volume. Moreover, declination of geomagnetic field line was examined from the imaged 3-D brightness distribution in the radar volume; the estimated declination of geomagnetic field line was in agreement with that computed from the International Geomagnetic Reference Field model in 2011. This paper has demonstrated some capabilities of radar imaging for ionospheric investigation. [ABSTRACT FROM AUTHOR]

In this paper, monitoring of metal-cased oil wells using the ultrawideband (UWB) radar is proposed. The inspection includes the detection and imaging of perforations and corroded areas in a metal pipe. Detection of small anomalies/ perforations on the surface of a narrow metal pipe is very challenging. Here, we present a method for imaging such small anomalies based on the extra time delay of the reflected pulse due to the effect of perforation in the radar near field. In this paper, the necessary concepts for the use of UWB radar specified for this application are developed and proved based on different measurement and simulation scenarios. We have experimentally demonstrated the effect of the perforations’ size on the time delay of reflected pulses. The distance between the perforation and the radar, for the near-field phenomenon, is critical for an effective detection and imaging. Therefore, we also studied the optimal distance between the radar and the perforation. Perforations with a size range of 1–3 cm are considered for the experiments and simulations. The experiments are done both in air and diesel. Synthetic aperture radar processing is used to reconstruct the images of the perforations and corroded area. Measurement and simulation results demonstrate the potential of UWB radar systems for oil well monitoring applications. [ABSTRACT FROM AUTHOR]

This paper discusses the intersatellite radiometric calibration for microwave sounders in support of NASA’s Global Precipitation Measurement (GPM) mission. This paper is an extension of previous GPM Intersatellite Calibration Working Group work, which assesses the robustness of the Central Florida Remote Sensing Laboratory “double difference” technique for sounders intercalibration. In this paper, we will analyze the intercalibration between the Advanced Technology Microwave Sounder on board the National Polar-orbiting Operational Environmental Satellite System Preparatory Project, the Microwave Humidity Sounder on board the MetOp-A and MetOp-B and NOAA-18 and NOAA-19, and the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie sounder onboard the Megha-Tropiques satellite. The sensitivity of the calibration to the geophysical parameters and incidence angles is investigated using two years data. Here, it is observed that a reliable intercalibration can be made only when the path lengths of the two sensors are similar, so when matching the scan angles for the two sensors, the sensitivity of the biases upon the water vapor profiles is significantly reduced. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spectral–spatial unified network (SSUN) with an end-to-end architecture for the hyperspectral image (HSI) classification. Different from traditional spectral–spatial classification frameworks where the spectral feature extraction (FE), spatial FE, and classifier training are separated, these processes are integrated into a unified network in our model. In this way, both FE and classifier training will share a uniform objective function and all the parameters in the network can be optimized at the same time. In the implementation of the SSUN, we propose a band grouping-based long short-term memory model and a multiscale convolutional neural network as the spectral and spatial feature extractors, respectively. In the experiments, three benchmark HSIs are utilized to evaluate the performance of the proposed method. The experimental results demonstrate that the SSUN can yield a competitive performance compared with existing methods. [ABSTRACT FROM AUTHOR]

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network that trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality that ensures a very good performance also in the presence of a mismatch with respect to the training set and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware. [ABSTRACT FROM AUTHOR]

Salt marsh vegetation extent and zonation are often controlled by bottom up factors determined in part by the frequency and duration of tidal inundation. Tidal inundation during remote-sensing mapping of salt marsh resources can alter the resulting image classification. The degree of this impact on mapping with very high resolution (VHR) imagery has yet to be determined. This paper utilizes topobathymetric light detection and ranging (LiDAR) data and bathtub models of a tidal stage at 5 cm intervals from mean low water (MLW) to mean high water (MHW) and determines the impact of tidal variation in salt marsh mapping within Jamaica Bay, NY, USA. Tidal inundation models were compared with the Worldview-2 and Quickbird-2 imageries acquired at a range of tidal stages. The modeled inundation of normalized difference vegetation index and smooth cordgrass (S. alterniflora) maps was compared from MLW to MHW. This paper finds that at 0.6 m above MLW, only 3.5% of S. alterniflora is inundated. This paper demonstrates a modeling approach integrating VHR satellite remote-sensing data and topobathymetric LiDAR data to address tidal variation in salt marsh mapping. The incremental modeling of the tidal stage is important for understanding areas most at risk from sea level rise and informs management decisions in accordance with this. [ABSTRACT FROM AUTHOR]

Similar to the conventional squint mode synthetic aperture radar (SAR) imaging processing, the estimation of Doppler centroid is one key problem for the low-squint-mode (within the range of [−5°, 5°]) multichannel in an azimuth high-resolution and wide-swath (HRWS) SAR system. In this paper, based on the electromagnetic scattering model, a novel weighted Doppler centroid estimation approach is proposed for the multichannel in the azimuth HRWS (MC-HRWS) SAR system. First, Maxwell’s equations are employed to derive the echo model for the multichannel SAR system. Then, an improved backscattering model is presented based on the small perturbation method and the available echo model, which is adopted to produce the weights for Doppler centroid estimation. More importantly, in order to improve the precision of Doppler centroid estimation, a weighted ambiguity-free Doppler centroid estimation approach is proposed, where the range-variant characteristic of Doppler centroid is employed to resolve the ambiguity number of Doppler centroid, and the echoes from different range bins with different signal-to-noise ratios are considered. In addition, the Cramer–Rao low bound of the estimated Doppler centroid is also discussed in this paper. The effectiveness of the proposed Doppler centroid estimation approach is verified via simulated and real measured low-squint-mode MC-HRWS SAR data. [ABSTRACT FROM AUTHOR]

The conventional strategy to download images captured by satellites is to compress the data on board and then transmit them via the downlink. It often happens that the capacity of the downlink is too small to accommodate all the acquired data, so the images are trimmed and/or transmitted through lossy regimes. This paper introduces a coding system that increases the amount and quality of the downloaded imaging data. The main insight of this paper is to use both the uplink and the downlink to code the images. The uplink is employed to send reference information to the satellite so that the onboard coding system can achieve higher efficiency. This reference information is computed on the ground, possibly employing extensive data and computational resources. The proposed system is called dual link image coding. As it is devised in this paper, it is suitable for Earth observation satellites with polar orbits. Experimental results obtained for data sets acquired by the Landsat 8 satellite indicate significant coding gains with respect to conventional methods. [ABSTRACT FROM AUTHOR]

This paper examines the potential in extracting the instantaneous location of maritime moving targets using a passive multistatic radar with Global Navigation Satellite Systems (GNSS) as illuminators of opportunity and a single receiver. This paper presents a theoretical framework for the localization of a moving target from a set of bistatic range measurements. The algorithm and its predicted accuracy are presented. The localization is achieved by what is essentially a multilateration technique, which can be applied while the transmitting platform is also in motion. The algorithms and the accuracy predictions, as a function of the number of transmitters, have been experimentally confirmed via a dedicated experimental campaign, where two different maritime targets were detected by up to 12 GNSS satellites belonging to different satellite constellations (GPS, GLONASS, and Galileo) simultaneously. To the best of the author’s knowledge, these are the first results of their kind and on this scale not only for GNSS-based passive radar but also for multistatic radar in general. [ABSTRACT FROM AUTHOR]

This paper presents a multidisciplinary research on the thermodynamic and geophysical effects of crude oil released underneath thin sea ice, and further evaluates the ability of a combined frequency- and time-domain approach toward its detection. To this end, a controlled oil release experiment in an artificially grown sea ice mesocosm was performed during the winter of 2017 at the Sea-Ice Research Environmental Facility located at the University of Manitoba. Ice cores extracted during the evolution of the sea ice prior and post oil injection allowed the investigation of the profile’s properties and the oil distribution. Furthermore, chemical composition and microstructure analysis were performed via a gas chromatography-time-of-flight mass spectrometry and X-ray, respectively. The time-series radar signature of the profile was measured utilizing ground penetration radar at 500 MHz and a C-band scatterometer. For this experiment, it was shown that the retrieval of the oil presence underneath the young sea ice layer was feasible, provided that the measured data were utilized simultaneously in a unified cost function. [ABSTRACT FROM AUTHOR]

Using mobile Light Detection and Ranging point clouds to accomplish road scene labeling tasks shows promise for a variety of applications. Most existing methods for semantic labeling of point clouds require a huge number of fully supervised point cloud scenes, where each point needs to be manually annotated with a specific category. Manually annotating each point in point cloud scenes is labor intensive and hinders practical usage of those methods. To alleviate such a huge burden of manual annotation, in this paper, we introduce an active learning method that avoids annotating the whole point cloud scenes by iteratively annotating a small portion of unlabeled supervoxels and creating a minimal manually annotated training set. In order to avoid the biased sampling existing in traditional active learning methods, a neighbor-consistency prior is exploited to select the potentially misclassified samples into the training set to improve the accuracy of the statistical model. Furthermore, lots of methods only consider short-range contextual information to conduct semantic labeling tasks, but ignore the long-range contexts among local variables. In this paper, we use a higher order Markov random field model to take into account more contexts for refining the labeling results, despite of lacking fully supervised scenes. Evaluations on three data sets show that our proposed framework achieves a high accuracy in labeling point clouds although only a small portion of labels is provided. Moreover, comparative experiments demonstrate that our proposed framework is superior to traditional sampling methods and exhibits comparable performance to those fully supervised models. [ABSTRACT FROM AUTHOR]

Permanent scatterer interferometry is a multitemporal interferometric synthetic aperture radar technique that produces high-accuracy ground deformation measurement. A high density of permanent scatterer (PS) is required to provide accurate results. In natural environments with low PS density, distributed scatterers (DSs) could serve as additional coherent observations. This paper introduces a polarimetric scattering property-based adaptive filtering method that preserves PS candidates and filters DS candidates. To further increase the coherence estimate of DS candidates, the technique includes a complex coherence decomposition that adaptively selects the most stable scattering mechanisms, thus improving pixel coherence estimation. The proposed method was evaluated on 11 quad-polarized ALOS PALSAR images and 21 dual-polarized Sentinel-1 images acquired over San Fernando Valley, CA, USA, and Groningen, The Netherlands, respectively. The application of this method increased the number of coherent pixels by almost a factor of eight compared with a single-polarization channel. This paper concludes that a coherence estimate can be significantly improved by applying scattering property-based adaptive filtering and coherence matrix decomposition and accurate displacement measurements can be achieved. [ABSTRACT FROM AUTHOR]

Recently, multifeature learning in collaborative representation classification (CRC) for hyperspectral images has generated promising performance. In this paper, two novel multifeature learning algorithms that update dictionary directly and indirectly are proposed. In order to offer the complementarity of multifeature, four different types of features—global feature (i.e., Gabor feature), local feature (i.e., local binary pattern), shape feature (i.e., extended multiattribute profiles), and spectral feature—are adopted in this paper. Under the hypothesis that most of the features should share the same coding pattern in CRC, this paper proposes to learn proper dictionaries for each feature until obtaining stable codes in a linear classifier. Furthermore, to avoid the explicit mapping of infinite-dimensional dictionaries in a nonlinear kernelized classifier, an indirect approach to construct the transformation matrix from original dictionaries to learn new dictionaries is developed. Three real hyperspectral images acquired from different sensors are adopted for performance evaluation. The experimental results demonstrate that the proposed methods can provide superior performance compared with those of the state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency’s ALOS-1/2 and NASA-ISRO’s NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring. [ABSTRACT FROM AUTHOR]

The accurate reconstruction of areas obscured by clouds is among the most challenging topics for the remote sensing community since a significant percentage of images archived throughout the world are affected by cloud covers which make them not fully exploitable. The purpose of this paper is to propose new methods to recover missing data in multispectral images due to the presence of clouds by relying on a formulation based on an autoencoder (AE) neural network. We suppose that clouds are opaque and their detection is performed by dedicated algorithms. The AE in our methods aims at modeling the relationship between a given cloud-free image (source image) and a cloud-contaminated image (target image). In particular, two strategies are developed: the first one performs the mapping at a pixel level while the second one at a patch level to take profit from spatial contextual information. Moreover, in order to fix the problem of the hidden layer size, a new solution combining the minimum descriptive length criterion and a Pareto-like selection procedure is introduced. The results of experiments conducted on three different data sets are reported and discussed together with a comparison with reference techniques. [ABSTRACT FROM AUTHOR]

The AisaOWL thermal hyperspectral instrument, manufactured by Specim, is a relatively new push-broom sensor well suited to airborne environmental surveys. The sensor covers the 7.6-12.6 μm part of the long-wave infrared region with 102 continuous bands, and is capable of imaging in low-light conditions. The detector array is a mercury cadmium telluride (MCT) semiconductor, which has an inherent randomly varying dark current for random pixels. This manifests in the raw data as a pixel switching between different intensity levels. These pixels are termed "blinkers" by the manufacturer. For each data acquisition, the pixels need to be tested for blinking behavior as different pixels are affected during each acquisition. However, little is known about the number of blink events, the duration of frames, or the optimal length of data acquisition. This paper presents the characterization of the blinking nature of pixels in the MCT detector array to provide guidance on data acquisition and processing. This paper finds that blinking behavior is not completely random, with some pixels more prone to blinking behavior than others. Most blinking pixels have only a few short blinks; therefore, there is still a considerable amount of good data in a blinking pixel. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has drawn considerable attention because of its extra degrees of freedom for high resolution and wide swath compared with the traditional multichannel SAR system. But how to extract the matched signal without the unmatched interferences is the foremost task for MIMO SAR system. In this paper, by using the orthogonal frequency division multiplexing chirp signals as the transmitted signals, it is demonstrated that the robust principal component analysis (RPCA) method can be successfully employed for ground moving target indication (GMTI) with no need for separating the matched signal and unmatched interferences. It is because the unmatched interference is proven to have low-rank property and noise-level magnitude, which can be separated apart from the matched signal with the RPCA method. However, the traditional RPCA methods may be restricted by the high computational burden due to the complex decompositions and multiple iterations. Hence, a fast interferometry RPCA method is proposed specially for GMTI mode, which takes full advantage of the characteristics of along-track interferometry SAR system. It can improve the probability of detection under low signal-to-clutter-and-noise ratio. Additionally, it will dramatically shorten the computational time. Furthermore, the proposed method can also estimate the radial velocities of the moving targets simultaneously. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this paper. [ABSTRACT FROM AUTHOR]

Surface roughness is a land-surface parameter that is widely used in terrain analysis. Some typical roughness details, which have important effects on surface analysis, fail to be characterized on previous roughness maps. The objective of this paper is to provide a more accurate small-to-large scale roughness overview. The new roughness method is designed based on a complete orthogonal system called the V-system. The V-system roughness utilizes the special functions to detect and extract the roughness characteristics from high-resolution digital elevation models (DEMs). In this paper, Lunar Orbiter Laser Altimeterderived DEMs are used as the source data for the roughness calculation. Compared with the global root-mean-square slope and Fourier-based roughness maps, the V-system roughness maps show that more typical roughness details have been added to clearly indicate the small roughness variations on the large map. Furthermore, the reliability and practicability of V-system roughness are demonstrated based on the multiresolution DEMs. As an example, the statistical parameters of the roughness characteristics in the lunar Maria and highlands identify the fact that the highlands are rougher at all scales than the Maria. And this difference corresponds to the basic roughness property. [ABSTRACT FROM AUTHOR]

This paper introduces an extension of morphological attribute profiles (APs) by extracting their local features. The so-called local feature-based APs (LFAPs) are expected to provide a better characterization of each APs' filtered pixel (i.e., APs' sample) within its neighborhood, and hence better deal with local texture information from the image content. In this paper, LFAPs are constructed by extracting some simple first-order statistical features of the local patch around each APs' sample such as mean, standard deviation, and range. Then, the final feature vector characterizing each image pixel is formed by combining all local features extracted from APs of that pixel. In addition, since the self-dual APs (SDAPs) have been proved to outperform the APs in recent years, a similar process will be applied to form the local feature-based SDAPs (LFSDAPs). In order to evaluate the effectiveness of LFAPs and LFSDAPs, supervised classification using both the random forest and the support vector machine classifiers is performed on the very high resolution Reykjavik image as well as the hyperspectral Pavia University data. Experimental results show that LFAPs (respectively, LFSDAPs) can considerably improve the classification accuracy of the standard APs (respectively, SDAPs) and the recently proposed histogram-based APs. [ABSTRACT FROM AUTHOR]