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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 continues an overview of the validation of operational profile retrievals from the Suomi National Polar-Orbiting Partnership (SNPP), with focus here given to the infrared (IR) ozone profile environmental data record (EDR) product. The SNPP IR ozone profile EDR is retrieved using the cross-track IR sounder (CrIS), a Fourier transform spectrometer that measures high-resolution IR earth radiance spectra containing atmospheric state information, namely, vertical profiles of temperature, moisture, and trace gas constituents. The SNPP CrIS serves as the U.S. low earth orbit (LEO) satellite IR sounding system and will be featured on future Joint Polar Satellite System (JPSS) LEO satellites. The operational sounding algorithm is the National Oceanic and Atmospheric Administration-Unique Combined Atmospheric Processing System (NUCAPS), a legacy sounder science team algorithm that retrieves atmospheric profile EDR products, including ozone and carbon trace gases, with optimal vertical resolution under nonprecipitating (clear to partly cloudy) conditions. The NUCAPS ozone profile product is assessed in this paper using extensive global in situ truth data sets, namely, ozonesonde observations launched from ground-based networks and from ocean-based intensive field campaigns, along with numerical weather prediction model output. Based upon rigorous statistical analyses using these data sets, the NUCAPS ozone profile EDRs are determined to meet the JPSS Level 1 global performance requirements. [ABSTRACT FROM AUTHOR]

On May 31, 2003, the scan line corrector (SLC) of the Enhanced Thematic Mapper Plus (ETM+) on-board the Landsat-7 satellite failed, resulting in strips of data lost in all ETM+ images acquired since then. In this paper, we proposed a novel inpainting algorithm for recovering the ETM+ SLC-off images. The two slopes of the boundaries of each missing stripe were extracted through the Hough transform, ignoring the slope of the edge of the strip that overlaps the edge of the image. An adaptive dictionary was then developed and trained using ETM+ SLC-on images acquired before May 31, 2003 so that the physical characteristics and geometric features of the ground coverage of the data-missing strips can be considered during recovery. To make the algorithm computationally efficient, data-missing strips were repaired along their slope directions by using the logdet $\left ({\cdot }\right)$ low-rank nonconvex model along with the dictionary. The algorithm was tested using the simulated ETM+ SLC-off images created from a multiband ETM+ SLC-on image file and compared to the high accuracy low-rank tensor completion (HaLRTC), logDet, and tensor nuclear norm (TNN) algorithms. The results show that the ETM+ images restored using the new algorithm have lower RMSE, higher PSNR and structure similarity (SSIM) values, and better visualization. These results indicate that the new algorithm performs better than the other three algorithms and can efficiently and accurately restore the data-missing stripes. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the surface waves investigation and monitoring (SWIM) instrument which will be one of the two payload instruments carried by China France Oceanography SATellite (CFOSAT) with a planned launch date in mid-2018. SWIM is a real aperture wave scatterometer operated at near-nadir incidence angles and dedicated to the measurement of directional spectra of ocean waves. The SWIM flight model is currently being assembled and tested, its performance is being assessed and its prototype data processing algorithm is being developed. The aim of this paper is to provide a complete overview on the motivations and scientific requirements of this mission, together with a description of the design and characteristics of the SWIM instrument, and the analysis of its expected performances based on a prelaunch study. An end-to-end simulator has been developed to evaluate the quality of the data products, thus allowing the overall performance of the instrument to be assessed. Simulations run with two subsets of full orbit subsets show that the performances of the instrument and the inversion algorithms will meet the scientific requirements for the mission. [ABSTRACT FROM PUBLISHER]

With the rapid increase of remote sensing images in temporal, spectral, and spatial resolutions, it is urgent to develop effective techniques for joint interpretation of spatial-temporal images. Multitype change detection (CD) is a significant research topic in multitemporal remote sensing image analysis, and its core is to effectively measure the difference degree and represent the difference among the multitemporal images. In this paper, we propose a novel difference representation learning (DRL) network and present an unsupervised learning framework for multitype CD task. Deep neural networks work well in representation learning but rely too much on labeled data, while clustering is a widely used classification technique free from supervision. However, the distribution of real remote sensing data is often not very friendly for clustering. To better highlight the changes and distinguish different types of changes, we combine difference measurement, DRL, and unsupervised clustering into a unified model, which can be driven to learn Gaussian-distributed and discriminative difference representations for nonchange and different types of changes. Furthermore, the proposed model is extended into an iterative framework to imitate the bottom-up aggregative clustering procedure, in which similar change types are gradually merged into the same classes. At the same time, the training samples are updated and reused to ensure that it converges to a stable solution. The experimental studies on four pairs of multispectral data sets demonstrate the effectiveness and superiority of the proposed model on multitype CD. [ABSTRACT FROM AUTHOR]

A rain-gauge-adjusted algorithm for global satellite mapping of precipitation (GSMaP) that estimates the surface precipitation rate with resolutions of 0.1° and 1 h over the globe is described herein. Precipitation is one of the most important parameters of the earth’s system, and its global distribution and changes are essential data for modeling the water cycle, maintaining ecosystems, increasing agricultural production, improving weather forecasting precision, and implementing flood warning systems. In the Global Precipitation Measurement project, integrated products of high-resolution mapping of precipitation, obtained from microwave measurements made by a constellation satellite and infrared radiometers in geostationary orbit, are developed and supplied to the public. However, these high-resolution products, such as GSMaP_MVK, sometimes underestimate surface precipitation, introducing large errors into hydrological modeling. This paper combines the global gauge data set with GSMaP_MVK, using a new algorithm [gauge-adjusted GSMaP (GSMaP_Gauge)], described and evaluated herein using local radar and rain-gauge data sets. This algorithm outperforms other GSMaP products in all validation tests. [ABSTRACT FROM AUTHOR]

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires: 1) two regression models in the forward and backward directions and 2) the estimation of statistical independence between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the sensitivity (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence, the criterion is robust to spurious detections. We illustrate the performance in a collection of 28 geoscience causal inference problems, a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and assessing the impact of different regression models in a carbon cycle problem. The criterion achieves the state-of-the-art detection rates in all cases, and it is generally robust to noise sources and distortions. The presented approach confirms the validity in observational bivariate problems in the earth sciences. [ABSTRACT FROM AUTHOR]

Relationships between vegetation indices (VIs) and leaf area index (LAI) tend to saturate in the nadir direction, and vary with crop canopy structure and view zenith angles (VZAs). The objective of this paper was to improve the monitoring accuracy and angular stability of VIs for estimating LAI using multiangular remote sensing data. The relationship between LAI and ground-based hyperspectral spectral reflectance was quantified in winter wheat (Triticum aestivum L.) exhibiting erectophile and planophile growth habits. To further reduce the saturation, species specificity, and angular sensitivity, we developed a saturation factor (SF), based on near-infrared and green bands. Multiplying all VIs by SF greatly improved the association with LAI across all VZAs ($R^{2} = 0.73{-}0.82$). Most VI $\times$ SF values, particularly optimized soil-adjusted VI $\times$ SF, were able to construct universal algorithms across VZAs for accurate estimation of LAI due to the sensitivity of SF to LAI in a dense canopy, and the insensitivity of SF to view effects with larger VZAs. This approach is also promising for the exploitation of multiangular satellite data for the design and calibration of nonview-angle-corrected spectral reflectance, for which the sensor is only deployed at fixed observation direction. [ABSTRACT FROM AUTHOR]

Multitemporal airborne and satellite imagery data with frequent repeat coverage provide great capability for change detection (CD). When comparing two images taken at different times of day or in different seasons for CD, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective CD algorithm, therefore, has to be robust to the illumination variation. In this paper, the robustness of phase correlation (PC) to shadow effects is proven via mathematical analysis, and then, an illumination-invariant change detection (IICD) metric is proposed based on pixel-wise dense PC matching. In the proposed IICD method, a graph-based visual saliency map is introduced for the initial CD followed by an active contour-based segmentation to precisely quantize the change region. Compared to the state-of-the-art CD algorithms, experiments using daily images of a landscape model and Landsat satellite images demonstrate that only the proposed method can effectively detect and precisely segment appearance changes under daily and seasonal sunlight changes. [ABSTRACT FROM AUTHOR]

The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: $R = 0.88$ , root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of $\pm 0.2\cdot {\mathrm {AOD}}_{\mathrm {AERONET}}\pm 0.05$. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land. [ABSTRACT FROM AUTHOR]

The airborne Operation IceBridge and spaceborne CryoSat-2 missions observe polar sea ice at different spatial and temporal scales as well as with different sensor suites. Comparison of data products from IceBridge and CryoSat-2 is complicated by the fact that they use different geophysical corrections: reference ellipsoid, geoid model, tide model, and atmospheric corrections to derive surface elevation and sea-ice freeboard. In this paper, we compare sea-ice surface elevation and freeboard using eight coincident CryoSat-2, Airborne Topographic Mapper (ATM), and Land, Vegetation, and Ice Sensor (LVIS) observations with direct IceBridge underflights of CryoSat-2 ground tracks. We apply identical geophysical corrections to CryoSat-2 and IceBridge data to eliminate elevation biases due to these differences and focus on differences due to retracker performance. The IceBridge ATM and LVIS elevation and freeboard and Snow Radar snow depth data sets are averaged to each CryoSat-2 footprint for comparison. With snow depth measurements, we are able to compare elevations and freeboards at the snow/ice interface for five different CryoSat-2 retrackers (ESA, GSFCv1, AWI, JPL, and GSFCv2) and IceBridge. The overall mean of freeboard differences between GSFCv2, ESA, AWI, JPL retrackers, and ATM are in agreement within 0.05 m. However, the five different CryoSat-2 retrackers show distinct differences in mean elevation over leads and over floes. This suggests that the physical interpretation of the different retrackers needs to be considered depending on usage, for example, elevations from CryoSat-2 retrackers need to be carefully calibrated before comparing with elevation from other satellites for long-term surface elevation trends. [ABSTRACT FROM AUTHOR]

Accurate information about ocean surface currents is of high importance for many purposes. Traditional ways of measuring surface currents using instruments immersed in water are associated with various challenges, as well as high installation and maintenance costs. Algorithms and hardware used to measure surface currents based on marine X-band radar images have been steadily improved during the recent decades. Such systems can now provide the user with current measurements with high accuracy in local areas of interest and with no need for underwater equipment, both from fixed sites and from moving installations, such as vessels. The primary focus of the work presented in this paper is to examine the measurement accuracy of an X-band radar-based system. For this purpose, field trials have been arranged and data acquired from a fixed platform in the North Sea in November 2015 and December 2015 and from a research vessel in the Norwegian Sea and the Barents Sea in November 2016. Data are analyzed, measured time series are compared to reference measurements and tidal current models, and statistical estimates of the measurement accuracy are calculated. Root-mean-square measurement errors of 0.032 m/s and 9.1° for magnitude and direction, respectively, are estimated for the fixed installation. Correlation coefficients are 0.93 and 0.94 for east–west and north–south components, respectively. A pooled standard deviation of 0.059 m/s is estimated for the moving installation. [ABSTRACT FROM AUTHOR]

This paper concerns two polarization orientation parameters of built-up areas derived from the polarimetric synthetic aperture radar (PolSAR) data considering two modeling orthogonal dihedral structures. The first orientation parameter is derived from the well-known circular polarization algorithm with the enrichment of arc distance median filtering using an adaptive neighborhood. The derivation of the second orientation parameter is realized by combining the slope-induced changes in polarimetric orientation angle with the shape-from-shading technique. The combination provides the possibility to measure the incidence angle and the azimuth component of the terrain slopes from the cross-pol SAR intensity image. With reference to the cross scattering model, a doubled cross scattering model (DCSM) is introduced by incorporating the orientation parameters, thus serving to guide the model-based decomposition. Using the DCSM refines the estimation of the cross-pol component by enabling us to further reveal the scattering characteristics of built-up areas. Following a novel criterion, the decomposition is implemented at two layers: one for urban areas and one for nonurban areas. The performance of parameter derivation is demonstrated and evaluated with airborne synthetic aperture radar, uninhabited aerial vehicle synthetic aperture radar, and GF-3 fully PolSAR data over different test sites. The decomposed results are consistent with the reference information provided by the National Land Cover Database 2011 about the land cover classification of test sites and encourage the use of the proposed decomposition scheme for different applications. [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]

In this paper, we discuss the structure and performance of the maximum entropy temperature-emissivity separation (MaxEnTES) algorithm for assessing land temperature and emissivity from thermal infrared hyperspectral images. This procedure derives the emissivity spectrum and the temperature of the target adopting the maximum entropy (MaxEnt) estimation approach. The main advantage of the MaxEnt statistical inference is the absence of any external hypothesis, which is, instead, the main critical point characterizing any other temperature-emissivity separation (TES) algorithm. The MaxEnTES algorithm carries out the TES task adopting a modified version of the subgradient Shor's r-algorithm adopted for numerical optimization of a MaxEnt objective function. For this purpose, we have utilized the C/C++ Solvopt code from the University of Gratz to develop a practical data processing implementation. In this paper, we discuss the mathematical structure of the MaxEnTES algorithm and analyze its performance in depth using numerical simulations and remote sensing Multispectral Infrared/Visible Imaging Spectrometer images. We show that the MaxEnTES algorithm provides improved accuracy for temperature and emissivity estimation, lowering the standard estimation error to a fraction of degree Kelvin. In agreement with previous investigations, we find that the estimation accuracy grows when increasing the number of available spectral channels. The systematic errors affecting the temperature estimates (e.g., bias) are thoroughly evaluated. We prove that the MaxEnTES algorithm retrieves the correct shape of the target emissivity spectrum even in presence of a significant temperature estimation error. [ABSTRACT FROM PUBLISHER]

Many hyperspectral anomaly detectors are designed based on the traditional Mahalanobis distance-based RX algorithm, which is usually considered as an inverse operation of the principal components analysis. Such detectors include the uniform target detector (UTD) algorithm, RX-UTD algorithm, and so on. However, the possibility of background statistical contamination caused by anomalies still exists. In order to alleviate this problem, in this paper, we propose a spectral matched filter (background-anomaly component projection and separation optimized filter) to minimize the average output energy of separate image components and the output values of the weighted background regular term for hyperspectral image anomaly detection, which could strengthen the separation between anomalies and backgrounds. By calculating the optimal solution to the background-anomaly component projection and separation function, we obtain the optimal projection, where we can effectively suppress the background while highlighting the anomalies. Proposed algorithm has the following research advantages: 1) it creates a novel collaborative component projection and robust background optimization function to separate the background and anomalies and 2) it analyzes the intrinsic statistical distribution of pixels and applies appropriate iterative shrinkage-thresholding algorithm to solve the $\ell _{1}$ -min problem. Experiments were conducted on three real hyperspectral data sets. The detection results demonstrate that the proposed algorithm is superior to other state-of-the-art anomaly detection algorithms. [ABSTRACT FROM AUTHOR]

This paper presents multiple kernel learning (MKL) in the context of remote sensing (RS) image classification problems by illustrating main characteristics of different MKL algorithms and analyzing their properties in RS domain. A categorization of different MKL algorithms is initially introduced, and some promising MKL algorithms for each category are presented. In particular, MKL algorithms presented only in machine learning are introduced in RS. Then, the investigated MKL algorithms are theoretically compared in terms of their: 1) computational complexities; 2) accuracy with different qualities of kernels; and 3) accuracy with different numbers of kernels. After the theoretical comparison, experimental analyses are carried out to compare differentMKL algorithms in terms of: 1) model selection and 2) feature fusion problems. On the basis of the theoretical and experimental analyses of MKL algorithms, some guidelines for a proper selection of the MKL algorithms are derived. [ABSTRACT FROM AUTHOR]

Because of flexible geometric configuration and trajectory designation, time-domain algorithms become popular for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) applications. In this paper, a new quasi-polar-coordinate-based fast factorized back-projection (FFBP) algorithm combined with data-driven motion compensation is proposed for miniature UAV-SAR imaging. By utilizing wavenumber decomposition, the analytical spectrum of a quasi-polar grid image is obtained, where the phase errors arising from the trajectory deviations can be conveniently investigated and the phase autofocusing can be compatibly incorporated. Different from the conventional FFBP based on a polar coordinate system, the proposed algorithm operates in a quasi-polar coordinate system, where the phase errors become spacial invariant and can be accurately estimated and easily compensated. Moreover, the relationship between phase errors and nonsystematic range cell migration (NsRCM) is revealed according to the analytical image spectrum, based on which the NsRCM correction is developed to further improve the image focusing quality for high-resolution SAR applications. Promising experimental results from the raw data experiments of miniature UAV-SAR test bed are presented and analyzed to validate the advantages of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Satellite remote-sensing data are essential for monitoring and quantifying water properties in the Great Lakes, providing useful monitoring and management tools for understanding water optical, biological, and ecological processes and phenomena. However, during the winter season, large parts of the Great Lakes are often covered by ice, which can cause significant uncertainties in satellite-measured water quality products. Although some developed radiance-based ice-detection algorithms for satellite ocean color data processing can eliminate most of the ice pixels in a region, there are still some significant errors due to misidentification of ice-contaminated pixels, particularly for the thin ice-covered regions. Therefore, it is necessary to improve the ice-detection methods for satellite ocean color data processing in the Great Lakes. In this paper, impacts of ice contamination on satellite-derived ocean color products in the Great Lakes are investigated, and a refined regional ice-detection algorithm which is based on the radiance spectra and normalized water-leaving radiance at the wavelength of 551 nm, nLw(551), is developed and assessed for satellite ocean color data processing in the Great Lakes. Results show that this proposed ice-detection method can reasonably identify ice-contaminated pixels, including those in very thin ice-covered regions, and provide accurate satellite ocean color products for the winter season in the Great Lakes. [ABSTRACT FROM PUBLISHER]

Estimation of the surface elevation change of the Greenland Ice Sheet (GrIS) is essential for understanding its response to recent and future climate change. Laser measurements from the NASA’s Ice, Cloud, and land Elevation Satellite (ICESat) created altimetric surveys of GrIS surface elevations over the 2003–2009 operational period of the mission. This paper compares four change detection methods using Release 634 ICESat laser altimetry data: repeat tracks (RTs), crossovers (XOs), overlapping footprints (OFPs), and triangulated irregular networks (TINs). All four methods begin with a consistently edited data set and yield estimates of volumetric loss of ice from the GrIS ranging from −193 to −269 km3/yr. Using a uniform approach for quantifying uncertainties, we find that volume change rates at the drainage system scale from the four methods can be reconciled within 1- $\sigma $ uncertainties in just 5 of 19 drainage systems. Ice-sheet-wide volume change estimates from the four methods cannot be reconciled within 1- $\sigma $ uncertainties. Our volume change estimates lie within the range of previously published estimates, highlighting that the choice of method plays a dominant role in the scatter of volume change estimates. We find that for much of the GrIS, the OFP and TIN methods yield the lowest volume change uncertainties because of their superior spatial distribution of elevation change rate estimates. However, the RT and XO methods offer inherent advantages, and the future work to combine the elevation change detection methods to produce better estimates is warranted. [ABSTRACT FROM PUBLISHER]

Image reranking is a popular postprocessing method for remote sensing image retrieval (RSIR), which aims at enhancing the initial retrieval performance. In general, it takes either users’ opinions or the relationships between images into consideration to find an optimal reranked list based on the initial retrieved results. In this paper, we present a reranking method for improving RSIR, which is named two-stage reranking (TSR). Suppose the k -nearest neighbors of a query RS image have been obtained by the initial retrieval. The first step of our TSR is to edit these neighbors using the editing scheme. A handful of informative and representative RS images are selected by the active learning algorithm, and their binary labels are provided by the users relative to the query image. Then, a binary classifier is trained using the selected RS images and their labels to classify the rest of the neighbors. Finally, both classification results and rank information in the initial retrieval results are considered to decide which neighbor should be excluded. In the next step, the remaining RS images are reranked by the proposed reranking scheme, i.e., multisimilarity fusion reranking. Both the user’s experience and image relationships are taken into account in TSR to ensure the performance of the reranking. The efficiency and the robustness of our method are validated by experiments conducted on two different types of RS images. Compared with the existing visual reranking approaches, our method achieves improved performance. [ABSTRACT FROM PUBLISHER]

In high spatial resolution (HSR) imagery scene classification, it is a challenging task to recognize the high-level semantics from a large volume of complex HSR images. The probabilistic topic model (PTM), which focuses on modeling topics, has been proposed to bridge the so-called semantic gap. Conventional PTMs usually model the images with a dense semantic representation and, in general, one topic space is generated for all the different features. However, this approach fails to consider the sparsity of the semantic representation, the classification quality, as well as the time consumption. In this paper, to solve the above problems, a fully sparse semantic topic model (FSSTM) framework is proposed for HSR imagery scene classification. FSSTM, with an elaborately designed modeling procedure, is able to represent the image with sparse but representative semantics. Based on this framework, the topic weights of multiple features are exploited by solving a concave maximization problem, which improves the fusion of the discriminative semantic information at the topic level. Meanwhile, the sparsity and representativeness of the topics generated by FSSTM guarantee that the image is adaptive to the change of a topic number. FSSTM can consistently achieve a good performance with a limited number of training samples, and is robust for HSR image scene classification. The experimental results obtained with three different types of HSR image data sets confirm that the proposed algorithm is effective in improving the performance of scene classification, and is highly efficient in discovering the semantics of HSR images when compared with the state-of-the-art PTM methods. [ABSTRACT FROM PUBLISHER]

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral–spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu’s method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. [ABSTRACT FROM PUBLISHER]

The concept of the “velocity synthetic aperture radar” (VSAR)—a multiaperture sensor capable of measuring radial velocities in the scene and utilizing this information to correct motion-induced imaging distortions inherent to SAR—was proposed two decades ago. Lately, with the emergence of truly multichannel systems featuring antenna arrays with dozens of elements, the approach has been enjoying a renewed interest. The viability and effectiveness of the algorithm were successfully demonstrated in a series of airborne field campaigns that involved imaging both man-made targets and natural maritime features. These experiments and the wealth of resulting data also underscored the need for comprehensive mathematical descriptions of expected target signatures in the collected “image stacks” and for further refinements of the VSAR imaging theory. This paper addresses both tasks by building upon the available mathematical results developed for the along-track interferometric SAR imagery of distributed evolving targets. The approach allows simultaneous accounting for all essential effects known to impact SAR imagery of a target or an extended feature: its azimuth velocity, radial velocity and acceleration, as well as finite coherence time. The emphasis is on obtaining closed-form expressions that could readily illustrate the structure and behavior of the VSAR stack spectrum of such a target and help gauge anticipated focusing improvement stemming from the VSAR image correction. In particular, it is rigorously shown that the VSAR algorithm is successful in situations when SAR defocusing arises predominantly from radial motion and short coherence times—the resulting resolution is generally no worse than that of the corresponding real-aperture radar. On the other hand, strong defocusing due to azimuth translation may be problematic to compensate within the VSAR approach framework. [ABSTRACT FROM PUBLISHER]

Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Through selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km2 acquired on June 21, 2014 and June 24, 2015 are presented. Data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation. [ABSTRACT FROM PUBLISHER]

Surface upward longwave radiation (SULR) is a significant component of the surface radiation budget and is closely linked with evapotranspiration, soil moisture, and surface cooling on clear nights. Therefore, accurately estimating SULR is essential to better understand its spatiotemporal dynamics or to characterize the thermal environment of a given land surface. Currently, most methods for estimating SULR (including the physical and hybrid methods) fail to account for the thermal anisotropy, which can introduce significant errors into the calculation. We previously proposed the combined algorithm that considers the thermal anisotropy to more accurately estimate the SULR. However, this proposed method has several shortcomings. For example, it considers the directionality of the emissivity and the effective temperature separately under the support of a parametric directional emissivity model. However, the directional emissivity model is not maturely developed for different land surface types, especially on nonvegetated surfaces. And the separation of land surface temperature and emissivity may undermine the estimation accuracy. Furthermore, this proposed method requires a series of input parameters that is not always available, limiting its applicability. In this paper, we present a refined algorithm that uses a kernel-driven model and the technique of band conversion to calculate the SULR directly based on surface-leaving radiances. This direct physical algorithm is then applied to the Wide-angle infrared Dual-mode line/area Array Scanner data set and validated using longwave radiation data collected by automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results of these tests suggest that the direct algorithm works effectively. The root-mean-square error (RMSE) and mean bias error (MBE) of the direct algorithm on maize surfaces are 4.417 and 0.474 \text W\cdot ~\text m^-2 , respectively. When the thermal anisotropy is incorporated, the RMSE and absolute MBE decrease by a maximum of 4.734 and 7.414 \mathrm W\cdot \mathrm m^\mathrm -2 , respectively. Different land types yield different results: for vegetable surfaces, the estimation biases of the direct model are approximately -2\mathrm W\cdot \mathrm m^\mathrm -2 , whereas orchard surfaces yield biases are between −2 and -3.5\mathrm W\cdot \mathrm m^-2 , and village surfaces yield biases exceeding - 10~\mathrm W\cdot \mathrm m^-2 . These differences can be attributed to the varying effects of the kernel-driven model across different types of land surfaces. The RMSE and absolute MBE obtained using the direct algorithm are slightly smaller (0.587 and 1.685 \mathrm W\cdot \mathrm m^\mathrm -2 , respectively) than those obtained using the combined algorithm; they are also smaller than the results of the traditional temperature–emissivity algorithm (by 8.7 and 11.7 \mathrm W\cdot \mathrm m^-2 , respectively). [ABSTRACT FROM PUBLISHER]

In this paper, we focus on tackling the problem of automatic accurate localization of detected objects in high-resolution remote sensing images. The two major problems for object localization in remote sensing images caused by the complex context information such images contain are achieving generalizability of the features used to describe objects and achieving accurate object locations. To address these challenges, we propose a new object localization framework, which can be divided into three processes: region proposal, classification, and accurate object localization process. First, a region proposal method is used to generate candidate regions with the aim of detecting all objects of interest within these images. Then, generic image features from a local image corresponding to each region proposal are extracted by a combination model of 2-D reduction convolutional neural networks (CNNs). Finally, to improve the location accuracy, we propose an unsupervised score-based bounding box regression (USB-BBR) algorithm, combined with a nonmaximum suppression algorithm to optimize the bounding boxes of regions that detected as objects. Experiments show that the dimension-reduction model performs better than the retrained and fine-tuned models and the detection precision of the combined CNN model is much higher than that of any single model. Also our proposed USB-BBR algorithm can more accurately locate objects within an image. Compared with traditional features extraction methods, such as elliptic Fourier transform-based histogram of oriented gradients and local binary pattern histogram Fourier, our proposed localization framework shows robustness when dealing with different complex backgrounds. [ABSTRACT FROM PUBLISHER]

For stepped frequency continuous wave ground penetrating radar (SFCW GPR), the image of buried targets is usually reconstructed by a combination of point-like scatters whose number is much smaller than that of pixels of target space image. The intrinsic sparseness of target space offers a migration imaging method to make the high-quality image of underground region based on compressive sensing (CS) theory. In this paper, the effects of air/ground interface and antenna beamwidth on CS-based SFCW GPR migration imaging are presented and analyzed. It is shown that the presence of the strong air/ground interface reflection and finite antenna beamwidth usually challenges the robust CS migration imaging algorithm in practice. To overcome this problem in the context of CS migration imaging, an improved CS migration imaging method for SFCW GPR system is proposed in this paper. Experimental results show that the approach is robust to deliver a high-quality image of underground region. [ABSTRACT FROM PUBLISHER]

In this paper, a real-time algorithm that operates on data provided by a network of weather radars in the form of moments is presented. The algorithm implementation, performance, and abilities are presented and discussed. As part of this paper, a methodology for mapping from native spherical coordinates to a common network grid is developed and demonstrated. Metrics which gauge the quality of mapping and quality of data are derived, and their use from a network point of view is demonstrated. The main product of the algorithm, which is an implementation of a vector velocity estimation technique based on a maximum likelihood approach, is shown, and its results are displayed, compared, and discussed. The application of a methodology for velocity unfolding and reflectivity display enhancement is presented as well. [ABSTRACT FROM AUTHOR]

The developments in sensor technology have made the high-resolution hyperspectral remote sensing data available to the remote sensing analyst for ground-cover classification and target recognition tasks. The inherent high dimensionality of such data sets and the limited ground-truth data availability in many real-life operating scenarios necessitate such hyperspectral classification systems to employ the dimensionality reduction algorithms. Previously, it has been shown that the addition of the spectral derivatives into the feature space improves the performance of the hyperspectral image analysis systems. Although the spectral derivative features are expected to provide additional information for the classification task at hand, the conventional classification techniques are typically not suitable for such fusion since simply combining these features would result in very high dimensional feature spaces, exacerbating the over-dimensionality problem. In this paper, we propose an effective approach for the decision-level fusion of the spectral reflectance information with the spectral derivative information for robust land cover classification. This paper differs from previous work because we propose effective classification strategies to alleviate the increased over-dimensionality problem introduced by the addition of the spectral derivatives for hyperspectral classification. The studies reported in this paper are conducted within the context of both single and multiple classifier systems that are designed to handle the high-dimensional feature spaces. The experimental results are reported with handheld, airborne, and spaceborne hyperspectral data. The efficacy of the proposed approaches (using spectral derivatives and single or multiple classifiers) as quantified by the overall classification accuracy (expressed in percentage) is significantly greater than that of these systems when exploiting only the reflectance information. [ABSTRACT FROM AUTHOR]

This paper presents two semisupervised one-class support vector machine (OC-SVM) classifiers for remote sensing applications. In one-class image classification, one tries to detect pixels belonging to one of the classes in the image and reject the others. When few labeled pixels of only one class are available, obtaining a reliable classifier is a difficult task. In the particular case of SVM-based classifiers, this task is even harder because the free parameters of the model need to be finely adjusted, but no clear criterion can be adopted. In order to improve the OC-SVM classifier accuracy and alleviate the problem of free-parameter selection, the information provided by unlabeled samples present in the scene can be used. In this paper, we present two state-of-the-art algorithms for semisupervised one-class classification for remote sensing classification problems. The first proposed algorithm is based on modifying the OC-SVM kernel by modeling the data marginal distribution with the graph Laplacian built with both labeled and unlabeled samples. The second one is based on a simple modification of the standard SVM cost function which penalizes more the errors made when classifying samples of the target class. The good performance of the proposed methods is illustrated in four challenging remote sensing image classification scenarios where the goal is to detect one of the classes present on the scene. In particular, we present results for multisource urban monitoring, hyperspectral crop detection, multispectral cloud screening, and change-detection problems. Experimental results show the suitability of the proposed techniques, particularly in cases with few or poorly representative labeled samples. [ABSTRACT FROM AUTHOR]

With the continuing increase in the number of images collected everyday from different sensors, the automated registration of multisensor/multispectral images has become a very important issue. This is particularly true when pre- and postevent image comparison is concerned: For this particular application, the requirement of obtaining the earliest possible postevent image imposes the use of data potentially possessing significantly different characteristics with respect to the pre-event image. Strongly inhomogeneous image pairs require robust automatic registration techniques, preferably based on resolutionindependent feature-based registration. In a previous paper, we proposed a mode-based feature-matching scheme mutated from the computer vision domain and adapted to pre- and postevent feature matching. Some of the weak points highlighted in that first version are addressed in this paper, where a new version of the method is proposed, which exploits a new piece of information, i.e., the adjacency between feature points, generally preserved across the disaster event. Extensive generation of synthetic cases allowed one to obtain significant feedback and, consequently, tune the algorithm. Three real cases of pre- and postevent feature matching on high-resolution satellite images are shown and discussed. [ABSTRACT FROM AUTHOR]

The combination of frequency-modulation continuous-wave (FMCW) technology and synthetic aperture radar (SAR) promises a lightweight, cost-effective, and high-quality imaging sensor for remote sensing. However, the long signal duration time leads to the failure of the conventional start/stop approximation of the pulsed SAR. In this paper, a signal model is proposed to address the effects of the continuous motion during the transmit time on the echoed signal. Based on the model, an analytical point target reference spectrum is derived. From the spectrum, it will be seen that the continuous motion introduces an additional range-azimuth coupling term and a range walk term compared with the conventional pulsed SAR. The range walk term is well known, whereas the foregoing range-azimuth coupling term is formulated for the first time in the FMCW SAR community. For the squint and spotlight modes, these range walk and range-azimuth coupling terms might significantly degrade the image quality. In this paper, based on the proposed analytical signal model, we further discuss the application of the wavenumber domain algorithm for the FMCW SAR data. In addition, different approximations of the Stolt mapping are made to highlight the effect of the range-dependent higher-order range-azimuth coupling terms on the 2-D impulse responses. Finally, X-band simulated experiments and Ka-band real FMCW SAR data are used to validate the signal model and the processing method. [ABSTRACT FROM AUTHOR]

This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g middotcm-2. For values higher than 3 g middotcm-2, errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K. [ABSTRACT FROM PUBLISHER]

This paper evaluates the performance of the global precipitation rate retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) that was described in Part I of this paper. AMSU is in polar orbit on several National Ocean and Atmospheric Administration (NOAA) operational weather satellites. Predicted rms retrieval errors based on a 15-km resolution 0.5-1.0-mm/h MMS truth were 0.88, 0.83, 1.13, and 3.04 for stratiform, warm rain, ice-free rain, and convective rain, respectively, which were averaged over all view angles for land and sea up to 730 latitude. For MM5 rates of 4-8 mm/h, these rms errors increased to 2.8, 3.4, 3.9, and 4.9 mm/h, respectively. The corresponding rms retrieval accuracies for MM5 hydrometeor water paths between 0.125 and 0.25 mm for rainwater, snow, and graupel were 0.19, 0.10, and 0.22 mm, respectively. The rms retrieval accuracy for the 0.125-0.25-m/s peak vertical wind was 0.08 m/s. Biases are small for cumulative precipitation estimates, although an upward correction factor of 1.37 is derived for convective precipitation rate probability distributions. Differences between these retrievals and those from the conically scanned Advanced Microwave Scanning Radiometer for the Earth Observing System instrument and an alternate NOAA AMSU algorithm are also characterized. [ABSTRACT FROM AUTHOR]

A 3-D inverse synthetic aperture radar imaging method for rapidly spinning targets, i.e., a generalized Radon transform (GRT)-CLEAN algorithm, is proposed in this paper. The signal model is first changed into an equivalent high-speed turntable model after the compensation of the translational motion. Second, based on the relationship between the range profile variation of the spinning targets and the scatterers' positions, the GRT is utilized to estimate the scatterers' positions. Finally, combining the GRT with the modified CLEAN approach, the parameters of each scatterer and, thus, the 3-D image of the targets can be obtained. In addition to the development of the GRT-CLEAN algorithm, the estimation and compensation of the linear translational motion error in the imaging process is also considered in this paper. Good images yielded confirm the effectiveness of the GRT-CLEAN algorithm in the simulations. [ABSTRACT FROM AUTHOR]

Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Some classifiers often require features that can be more easily obtained by extracting scattering centers from ISAR data rather than by reconstructing ISAR images. An available method for scattering center extraction, namely, the CLEAN technique, was proposed in a recent paper by Yang et al. In this paper, an improvement of this CLEAN technique is proposed that introduces a new method for detecting scattering centers. The proposed technique is based on a Gaussianity test, and its effectiveness is first theoretically proven and then tested on real data. Moreover, a comparison with the technique proposed by Yang et al. is shown. [ABSTRACT FROM AUTHOR]

This paper addresses the capability of synthetic aperture radar and optical images in combination with theoretical models to detect the vegetation water content (VWC) at field level. In this paper, a retrieval algorithm for the estimation of VWC from AirSAR acquired on vegetated fields during the SMEX'02 experiment is addressed. The aforementioned campaign has been chosen because, along with sensor observations, extensive ground truth measurements were acquired. The retrieval procedure, which is based on a Bayesian approach, has been initially developed for soil moisture extraction, It consists of two modules: one is pertinent to bare soils and the other one has been modified for vegetated fields. The last one uses the synergy with optical images to correct for the contribution of VWC. The VWC, a variable in the inversion procedure, as well as soil moisture can be estimated. The results indicate a good correlation with both ground measurements and VWC calculated from Landsat images through the use of normalized difference water index (NDWI). Furthermore, in the inversion procedure, the introduction of the dependence on roughness improves the estimates. This indicates that, even for dense vegetation, the contribution from bare soil greatly influences the radar signal. Three main levels of VWC are discriminated in the inversion procedure: values below 1 kg/m², values between 1 and 3 kg/m², and values greater than 3 kg/m². [ABSTRACT FROM AUTHOR]

Bistatic synthetic aperture radar (SAR) uses a separated transmitter and receiver flying on different platforms to achieve benefits like exploitation of additional information contained in the bistatic reflectivity of targets, reduced vulnerability for military applications, forward-looking SAR imaging, or increased radar cross section. Besides technical problems such as synchronization of the oscillators, involved adjustment of transmit pulse versus receive gate timing, antenna pointing, flight coordination, and motion compensation, the development of a bistatic focusing algorithm is still in progress and not sufficiently solved. As a step to a numerically efficient processor, this paper presents a bistatic range migration algorithm for the translationally invariant case, where transmitter and receiver have equal velocity vectors. In this paper, the algorithm was successfully applied to simulated and real bistatic data. The real bistatic data have been acquired with the Forschungsgesellschaft für Angewandte Naturwissenschaften (FGAN)'s X-band SAR systems, namely the Airborne Experimental Radar II and the Phased Array Multifunctional Imaging Radar, in October 2003. [ABSTRACT FROM AUTHOR]

Spatial resolution is a key parameter of all remote sensing satellites and platforms. The nominal spatial resolution of satellites is a well-known characteristic because it is directly related to the area in ground that represents a pixel in the detector. Nevertheless, in practice, the actual resolution of a specific image obtained from a satellite is difficult to know precisely because it depends on many other factors such as atmospheric conditions. However, if one has two or more images of the same region, it is possible to compare their relative resolutions. In this paper, a wavelet-decomposition-based method for the determination of the relative resolution between two remotely sensed images of the same area is proposed. The method can be applied to panchromatic, multispectral, and mixed (one panchromatic and one multispectral) images. As an example, the method was applied to compute the relative resolution between SPOT-3, Landsat-5, and Landsat-7 panchromatic and multispectral images taken under similar as well as under very different conditions. On the other hand, if the true absolute resolution of one of the images of the pair is known, the resolution of the other can be computed. Thus, in the last part of this paper, a spatial calibrator that is designed and constructed to help compute the absolute resolution of a single remotely sensed image is described, and an example of its use is presented. [ABSTRACT FROM AUTHOR]

This paper develops a critical review of the hyperspectral splitting of the solar reflected radiation acquired by hyperspectral imaging sensors. The bandwidth used in the range from 2.0-2.5 μm by many hyperspectral sensors sometimes is too narrow for land cover classification. In fact, hyperspectral imagers often suffer from low signal-to-noise (SNR) in the short-wave infrared region of the electromagnetic spectrum, resulting in noisy image collection. This paper presents a new methodological approach to the splitting of the solar reflected radiation, called the "superspectral approach." It is based on the principle of increasing the channel bandwidth by increasing the number of wavelengths, to build synthetic spectral bands with higher SNRI The method- ology has been applied to the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, an airborne hyperspectral scanner used for environmental remote sensing applications in Italy. Interesting results have been achieved in crop classification, processing the Cordenons survey carried out in August 2001 in the northeastern part of Italy. The Spectral Angle Mapper algorithm was used for classification because it is insensitive to shadows. For accuracy assessment, the overall accuracy (OA) and kappa coefficient (k) were calculated and used in the comparison. Using the superspectral approach, an increment in the overall accuracy of about 42% and an increment in the kappa coefficient of about 51 % were obtained in comparison to the classification accuracy of unprocessed original MINIS data (OA = 41.21, k = 0.35). A second case study is presented using the National Aeronautics and Space Administration's experimental hyperspectral imager HYPERION. Data acquired over the lake of Garda (Italy) in October 2002 was processed with the superspectral approach. Comparing the simulated HYPERION superspectral bands with the original data, SNR improvements are achieved in the shortwave infrared region (from 0.7-54.2 for 2.012-μm wavelength and from 0.7-64.5 for the 2.365-μm wavelength). The methodology proposed is sensor independent and can be applied to any of the hyperspectral sensors currently available. [ABSTRACT FROM AUTHOR]

The goal of the project described in this paper is to build a prototype of an operational system, which will provide registration within subpixel accuracy of multitemporal Landsat data, acquired by either Landsat-5 or Landsat-7 Thematic Mapper instruments. Integrated within an automated mass processing system for Landsat data, the input to our registration system consists of scenes that have been geometrically and radiometrically corrected, as well as preprocessed for detection of clouds and cloud shadows. Such preprocessed scenes are then georegistered relative to a database of Landsat chips. This paper describes the entire registration process, including the use of landmark chips, feature extraction performed by an overcomplete wavelet representation, and feature matching using statistically robust techniques. Knowing the approximate longitudes and latitudes or the UTM coordinates of the four corners of each incoming scene, a subset of the chips that represent landmarks included in the scene are selected to perform the registration. For each of these selected landmark chips, a corresponding window is extracted from the incoming scene, and each chip-window pair is registered using a robust wavelet feature-matching methodology. Based on the transformations from the chip-window pairs, a global transformation is then computed for the entire scene using a variant of a robust least median of squares estimator. Empirical results of this registration process, which provided subpixel accuracy for several multitemporal scenes from different study areas, are presented and discussed. [ABSTRACT FROM AUTHOR]

The problem of temperature and spectral emissivity assessment from hyperspectral remotely sensed data is discussed with reference to monitoring of active fires and hot targets. A new algorithm, called similar pixel addition, was developed, which allows us to retrieve the temperature of burning areas by employing spectral data collected at thermal infrared (TIR) wavelengths. The new algorithm resolves the uncertainty connected with temperature-emissivity separation assuming a slow spatial variation of emissivity, hence reducing the number of unknowns involved in the inversion of a couple of similar pixels at once. Performance of this procedure is thoroughly discussed and compared with results from two other algorithms operating in the TIR and shortwave infrared spectral ranges. This paper shows results obtained applying the new algorithm to hyperspectral images gathered by the Multispectral Infrared and Visible Imaging Spectrometer in Northern Italy (Alps) over a natural fire that broke out in July 1999. This paper is completed with a theoretical discussion of the involved topics. [ABSTRACT FROM AUTHOR]

In this paper, a new approach based on two fusion schemes is proposed to overcome the uncertainties in land surface emissivity (LSE) estimation and, consequently, land surface temperature (LST) retrieval. The fusion schemes are called image-based weighted methods and knowledge-based weighted methods, in which each of them includes two LSE estimation methods. The effectiveness of the two proposed fusion schemes is empirically tested over two scenes of Landsat-8 (known as Landsat Data Continuity Mission) data sets, and the obtained LSEs by individual and proposed methods were compared to the LSE product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) by image-based and class-based cross-comparison. In both scenes, the adjusted normalized emissivity method (ANEM) and NDVI-based emissivity method (NBEM) provide appropriate results among five individual methods. In contrast, weighted to median (WMED) achieves superior results among the proposed fusion methods for both scenes. In addition, the root-mean-square error (rmse) values of LSE obtained by ANEM and WMED are 1.48% and 0.87%, which lead to 1.25 K and 0.73 K errors in the LST retrieval by the single-channel algorithm in the first scene, respectively. For the second scene, the error values of NBEM and WMED are 1.10% and 0.52%, which lead to 0.93 K and 0.44 K errors in the LST, respectively. Moreover, the error ranges and rmse of cross-comparison for the obtained LSE in the proposed methods were remarkably decreased. Also, in this research, for LST cross-comparison, an alternative scaling method based on LST products of the Moderate Resolution Imaging Spectroradiometer was proposed. The LST validation results demonstrated that the proposed methods provide better estimates in terms of three accuracy measures in both examined data sets. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for the retrieval of ozone profiles from the Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership satellite using a nonlinear optimal estimation method. The issue of channel selection is discussed. Based on a sensitivity analysis, we selected a spectral range of 990–1070 cm−1 for the ozone profile retrievals. Compared with ERA-Interim ozone profiles and eigenvector regression method profiles, the ozone climatology profile is better able to construct the a priori state. The retrieved CrIS profiles are in good agreement with smoothed high-vertical-resolution ozonesonde profiles. An analysis of the information content of the CrIS retrievals demonstrates that the CrIS measurements can provide useful information for capturing the spatial and temporal variations in ozone and are insensitive to ozone below 400 hPa. An error analysis revealed that smoothing error represents the main error source for the retrieved CrIS ozone profiles. [ABSTRACT FROM AUTHOR]

In this paper, the retrieval of sea surface current velocity from vertically polarized (V-pol) X-band marine radar data is presented. Three different methods, including the iterative least square approach, the normalized scalar product method, and the polar current shell algorithm, that have been used for horizontally polarized data are employed here. A comprehensive comparison of the performance of the three methods is conducted using the radar images collected by the V-pol radar deployed on the Forschungsplattformen in Nord- und Ostsee No. 3 (FINO3) offshore research platform and the acoustic Doppler current profiler (ADCP) data in the North Sea. The results indicate that all three methods are capable of providing reliable current speed and direction measurements from V-pol data, with similar performance. Based on the experimental data for which the current magnitude is less than 0.5 m/s, the radar-derived results agree best with the ADCP data at a depth of 6–8 m, with the root mean square difference for current velocity $x$- and $y$-components being 7.2–8.9 cm/s. The correlation coefficients between the radar-derived and ADCP-measured current velocity components are as high as 0.87–0.93. [ABSTRACT FROM AUTHOR]

This paper presents a kernel-based feature selection method for the classification of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant (discriminant) for the considered classification problem, i.e., preserve the functional relationship between input and output variables, and 2) invariant (stable) across different domains, i.e., minimize the data-set shift between the source and the target domains. Domains can be associated with hyperspectral images collected either on different geographical areas or on the same area at different times. We propose a novel measure of data-set shift for evaluating the domain stability, which computes the distance of the conditional distributions between the source and target domains in a reproducing kernel Hilbert space. Such a measure is defined on the basis of the kernel embeddings of the conditional distributions resulting in a nonparametric approach that does not require estimating the distribution of the classes. The adopted search strategy is based on a multiobjective optimization algorithm, which optimizes the two terms of the criterion function for the estimation of the Pareto-optimal solutions. This results in an effective approach of performing feature selection in a transfer learning setting. The experimental results obtained on two hyperspectral images show the effectiveness of the proposed method in selecting features with high generalization capabilities. [ABSTRACT FROM AUTHOR]

Numerous observations and modeling results have shown that there is noticeable directional variation in the solar-induced chlorophyll fluorescence (SIF), and this has not been well investigated. In this paper, 16 multiangular spectral observations were carried out on winter wheat to assess the bidirectional SIF emission. First, the bidirectional SIF emission was retrieved from the spectral measurements made by a high-performance QE Pro spectrometer and an automatic multiangle observation system using the 3FLD algorithm. The bidirectional shape of the SIF emission was found to be similar to that of the canopy reflectance in the solar principal plane, with a mean correlation coefficient of 0.94 and 0.97 at the O2-B and O2-A bands, respectively. The modified Rahman–Pinty–Verstraete (MRPV) model, a semiempirical bidirectional reflectance distribution function (BRDF) model, was then employed to describe the bidirectional variation in the SIF and reflectance with a mean root-mean-square-error value of 0.036 and 0.041 mW m−2sr−1nm−1 for the SIF at the O2-B and O2-A bands, respectively. Finally, both the bidirectional reflectance and SIF were BRDF corrected to nadir using the MRPV model. Most of the directional variation was successfully corrected by this method—the mean correction ratios were 87% and 81% for the reflectance at the O2-B and O2-A bands and 84% and 72% for the SIF at the O2-B and O2-A bands, respectively. Therefore, the SIF emission cannot be regarded as isotropic, and the high similarity between the bidirectional SIF and reflectance, together with the BRDF correction results, indicates that the bidirectional SIF emission can be adjusted using either the BRDF reflectance models or prior knowledge. [ABSTRACT FROM AUTHOR]

Daily snow cover mapping is difficult when Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are cloud obscured. The daily cloud-free snow cover product provides an essential parameter for hydrological modeling, climate system studies, and snow-caused disaster monitoring on the Tibetan Plateau (TP). In this paper, we present an algorithm, Terra–Aqua–IMS (TAI), which combines MODIS Terra and Aqua (500 m) and the Interactive Multisensor Snow and Ice Mapping System (IMS; 4 km) to produce a daily cloud-free snow cover product (500 m). The overall accuracy of the new TAI over the TP is 94% as compared with ground stations in all-sky conditions; this value is significantly higher than the 64% of the blended MODIS Terra–Aqua product and the 55% and 50% of the original MODIS Terra and Aqua products, respectively. Without the IMS, the daily combination of MODIS Terra–Aqua can only remove limited cloud contamination: 37.3% of the annual mean cloud coverage compared with 46.6% (MODIS Terra) and 55.1% (MODIS Aqua). The resulting annual mean snow cover over the TP from the daily TAI data is 19.1%, which is much larger than the 4.7%–8.1% from the daily original MODIS Terra/Aqua and the blended Terra–Aqua snow product due to cloud blockage. [ABSTRACT FROM PUBLISHER]

Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps. [ABSTRACT FROM PUBLISHER]