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The 28 papers in this special issue present a collection of papers focusing on the application of remote sensing to urban areas. [ABSTRACT FROM PUBLISHER]

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a small number of noisy linear measurements is an important problem in compressed sensing. In this paper, the high-dimensional setting is considered. It is shown that if the measurement rate and per-sample signal-to-noise ratio (SNR) are finite constants independent of the length of the vector, then the optimal sparsity pattern estimate will have a constant fraction of errors. Lower bounds on the measurement rate needed to attain a desired fraction of errors are given in terms of the SNR and various key parameters of the unknown vector. The tightness of the bounds in a scaling sense, as a function of the SNR and the fraction of errors, is established by comparison with existing achievable bounds. Near optimality is shown for a wide variety of practically motivated signal models. [ABSTRACT FROM PUBLISHER]

Techniques for retrieving cloud optical properties, i.e., the optical depths and particle size distributions, using atmospheric "window" channels in the visible and near-infrared spectral regions are well established. For partially transparent thin cirrus clouds, these "window" channels receive solar radiances scattered by the surface and lower level water clouds. Accurate retrieval of optical properties of thin cirrus clouds requires proper modeling of the effects from the surface and the lower level water clouds. In this paper, we describe a new concept using two strong water vapor absorption channels near 138 and 1.88 μm, together with one window channel, for remote sensing of cirrus optical properties. Both the 138- and 1.88-μm channels are highly sensitive in detecting the upper level cirrus clouds. Both channels receive little scattered solar radiances from the surface and lower level water clouds because of the strong water vapor absorption below cirrus. The 1.88-μm channel is quite sensitive to changes in ice particle size distributions, while the 138-μm channel is less sensitive. These properties allow for simultaneous retrievals of optical depths and particle size distributions of cirrus clouds with minimal contaminations from the surface and lower level water clouds. Preliminary tests of this new concept are made using hyperspectral imaging data collected with the Airborne Visible Infrared Imaging Spectrometer. The addition of a channel near 1.88 μm to future multichannel meteorological satellite sensors would improve our ability in global remote sensing of cirrus optical properties. [ABSTRACT FROM AUTHOR]

In this paper, a new joint spectral–spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this paper, a concept of subpixel abundance map, which calculates the abundance fraction of each subpixel to belong to a given class, was introduced. This allows us to directly connect the original (coarser) hyperspectral image with the final subpixel result. Furthermore, the proposed approach incorporates the spectral information contained in the original hyperspectral imagery and the concept of spatial dependence to generate a final subpixel mapping result. The proposed approach has been experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method achieves better results when compared to other seven subpixel mapping methods. The numerical comparisons are based on different indexes such as the overall accuracy and the CPU time. Moreover, the obtained results are statistically significant at 95% confidence. [ABSTRACT FROM AUTHOR]

Beginning with 2013 IEEE Geoscience and Remote Sensing Society has a new publication: the IEEE Geoscience and Remote Sensing Magazine. The magazine provides a new venue to publish high quality technical articles that by their very nature do not find a home in journals requiring scientific innovation but that provide relevant information to scientists, engineers, end-users, and students who interact in different ways with the geoscience and remote sensing disciplines. The magazine will publish tutorial papers and technical papers on geoscience and remote sensing topics, as well as papers that describe relevant applications of and projects based on topics addressed by our society. This call for papers is to encourage all readers to prepare and submit articles and technical content for review to be published in the IEEE Geoscience and Remote Sensing Magazine. Contributions for the above-mentioned columns of the magazine are also welcome. All technical papers will undergo blind review by multiple reviewers. The submission and the review process are managed at the IEEE Manuscript Central as it is already done for the three GRSS journals. Prospective authors are required to submit electronically using the website http://mc.manuscriptcentral.com/grs and selecting the "Geoscience and Remote Sensing Magazine" option from the drop-down list. Instructions for creating new user accounts, if necessary, are available on the login screen. No other manners of submission are accepted. Papers should be submitted in single column, double-spaced format. The review process will assess the technical quality and/or the tutorial value of the contributions. The magazine will publish also special issues. Readers who are interested to propose a special issue can contact the Editor-in-Chief (EiC). [ABSTRACT FROM PUBLISHER]

Deep neural networks (DNNs), including convolutional neural networks (CNNs) and residual networks (ResNets) models, are able to learn abstract representations from the input data by considering a deep hierarchy of layers that perform advanced feature extraction. The combination of these models with visual attention techniques can assist with the identification of the most representative parts of the data from a visual standpoint, obtained through more detailed filtering of the features extracted by the operational layers of the network. This is of significant interest for analyzing remotely sensed hyperspectral images (HSIs), characterized by their very high spectral dimensionality. However, few efforts have been conducted in the literature in order to adapt visual attention methods to remotely sensed HSI data analysis. In this paper, we introduce a new visual attention-driven technique for the HSI classification. Specifically, we incorporate attention mechanisms to a ResNet in order to better characterize the spectral–spatial information contained in the data. Our newly proposed method calculates a mask that is applied to the features obtained by the network in order to identify the most desirable ones for classification purposes. Our experiments, conducted using four widely used HSI data sets, reveal that the proposed deep attention model provides competitive advantages in terms of classification accuracy when compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This article reports on the IEEE Transactions on Geoscience and Remote Sensing, a monthly publication for applied and theoretical papers on geoscience and remote sensing. It provides information for authors regarding the subject matter, style, submission of manuscripts, references and figure captions, page numbers, review process, and copyright.

The papers in this special section examine the use of remote sensing technology to promote environmental sustainability in Asia-Pacific regions. Worldwide urbanization and deforestation are the two main interconnected ways that human activities are continually changing and reshaping the earth's surface. How earth observation and remote sensing technologies can contribute to improve the knowledge of the productivity and sustainability of natural and human ecosystems is an important theme in the global change community. In China, for instance, rapid economic growth and urbanization over the past three decades have resulted in dramatic changes in land use and land cover and have led to severe environmental consequences, which have made China's sustainable development a grand challenge. In the meantime, during the past few decades, environmental changes in the Asian–Pacific region have posed significant challenges to the scientific community. Therefore, the global problem of how earth observation and remote sensing technologies may be applied to assessing, monitoring, modeling, and simulating ecosystems, environments, and resources at various spatial and temporal scales translates into peculiar and very urgent questions and applications in this colossal and dynamic geographical region. [ABSTRACT FROM AUTHOR]

This paper aims to achieve computationally efficient and high-accuracy subpixel image registration with large displacements under the rotation–scale–translation model. This paper employs the classical phase correlation algorithm and the Lucas–Kanade (LK) algorithm in a two-stage coarse-to-fine framework, for which the motivation is from the observation that the two algorithms exhibit strong complementary property between convergence range and subpixel accuracy. In this framework, the LK algorithm will also become computationally efficient owing to the small residual displacement. On the other hand, this paper takes into account the residual model with respect to the compensation scheme explicitly, and deduces formulas for the final results combination, which is expected to be closer to the true displacement vector and thus further improve the estimation accuracy. Since the compensation can be applied to either the target image or the reference image, two algorithms are presented accordingly, and analysis as well as comparison are also performed. Finally, both simulations and real image experiments are performed to verify the motivation, and the results are consistent with the analysis. [ABSTRACT FROM PUBLISHER]

The papers in this special section examine recent advancements in the processing of high spatial resolution (HSR) remote sensing data. Since HSR remote sensing satellite (IKONOS, in September 1999), we have been entering a new era of high-resolution earth observation (EO). A variety of high-resolution remote sensing platforms continue to emerge. For instance, the new generation of theWorldView-3/4 satellites can provide high-resolution and super-spectral images, and their extraordinary ability allows them to simultaneously and accurately depict the spectral-spatial properties of the land surface. [ABSTRACT FROM AUTHOR]

The practical application of a new class of coprime arrays based on the Chinese remainder theorem (CRT) over quadratic fields is presented in this paper. The proposed CRT arrays are constructed by ideal lattices embedded from coprime quadratic integers with $\mathbf {B}_1$ and $\mathbf {B}_2$ being their matrix representations, respectively, whereby the degrees of freedom (DOF) surges to $O(|\det {(\mathbf {B}_1\mathbf {B}_2)}|)$ with $|\det (\mathbf {B}_1)| + |\det (\mathbf {B}_2)|$ sensors. The geometrical constructions and theoretical foundations were discussed in the accompanying paper in great detail, while this paper focuses on aspects of the application of the proposed arrays in two-dimensional (2-D) remote sensing. A generalization of CRT arrays based on two or more pairwise coprime ideal lattices is proposed with closed-form expressions on sensor locations, the total number of sensors, and the achievable DOF. The issues pertaining to the coprimality of any two quadratic integers are also addressed to explore all possible ideal lattices. Exploiting the symmetry of lattices, sensor reduction methods are discussed with the coarray remaining intact for economic maximization. In order to extend conventional angle estimation techniques based on uniformly distributed arrays to the method that can exploit any coarray configurations based on lattices, this paper introduces a hexagon-to-rectangular transformation to 2-D spatial smoothing, providing the possibility of finding more compact sensor arrays. Examples are provided to verify the feasibility of the proposed methods. [ABSTRACT FROM AUTHOR]

Image segmentation techniques are challenging to apply to large-size remote sensing imagery. Indeed, if the data to be processed are larger than the computer’s available memory, it must be split into smaller pieces. Without precaution, segmentation errors appear along the edges of these pieces. The goal of this paper is to present a tilewise processing method to overcome this issue for superpixel segmentation, applied in particular to the simple linear iterative clustering algorithm. Incidentally, tilewise methods allow for several pieces of the image to be processed simultaneously, which enables the deployment of these methods in a parallel processing environment. Estimations of the speed-up when using multiple processors are provided. Then, it is demonstrated that the result of the tilewise segmentation is equivalent to the segmentation of the complete image, with respect to a number of global unsupervised segmentation criteria. Finally, experimental results on a large-size Sentinel-2 time series validate the method’s feasibility. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with “one-window-one-car” results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Remote sensing satellites can provide a large number of multispectral images. However, due to the limitations of optical sensors embedded in satellites, the spatial resolution of multispectral images is relatively low. Pansharpening aims to combine high-resolution panchromatic and multi-spectral images to generate high-resolution multi-spectral images. In this paper, we propose a pansharpening method based on a component substitution framework. We use fractional-order differential operators and guided filter to balance the spectral distortion and spatial information loss that occur when remote sensing image fusion. Fractional-order differentiation can better define the detailed map, and the guided filter can enhance the spectral information of the detailed map. Experiments show that the proposed method in this paper can better combine the spectral information and spatial information, as well as obtain satisfactory results in both subjective visual perception and objective object evaluation. [ABSTRACT FROM AUTHOR]

An introduction for the June 2007 issue of the "IEEE Geoscience and Remote Sensing Society" is presented.

The article presents information on the 2003 IEEE Geoscience and Remote Sensing Society's (GRS-S) Awards that were presented at the IGARSS'03 banquet on July 24 at Hotel Dieu in Toulouse, France. Charles Luther, the president of the society, presented those awards with the assistance of the Awards Committee Chairman Werner Wiesbeck. The award ceremony was started with the recognition of the IEEE Fellows who have declared the GRS-Society as their home society. The GRS-S Transactions Editor, based on recommendations of associate editors and reviewers, compiled the list of nominees for the Transactions Prize Paper Award.

Forward-looking ground-penetrating radar (FLGPR) has recently been investigated as a remote-sensing modality for buried target detection (e.g., landmines). In this context, raw FLGPR data are beamformed into images, and then, computerized algorithms are applied to automatically detect subsurface buried targets. Most existing algorithms are supervised, meaning that they are trained to discriminate between labeled target and nontarget imagery, usually based on features extracted from the imagery. A large number of features have been proposed for this purpose; however, thus far it is unclear as to which are the most effective. The first goal of this paper is to provide a comprehensive comparison of detection performance using existing features on a large collection of FLGPR data. Fusion of the decisions resulting from processing each feature is also considered. The second goal of this paper is to investigate two modern feature learning approaches from the object recognition literature: the bag-of-visual words and the Fisher vector for FLGPR processing. The results indicate that the new feature learning approaches lead to the best performing FLGPR algorithm. The results also show that fusion between existing features and new features yields no additional performance improvements. [ABSTRACT FROM AUTHOR]

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]

Owing to the limits of incident energy and hardware system, hyperspectral (HS) images always suffer from low spatial resolution, compared with multispectral (MS) or panchromatic (PAN) images. Therefore, image fusion has emerged as a useful technology that is able to combine the characteristics of high spectral and spatial resolutions of HS and PAN/MS images. In this paper, a novel HS and PAN image fusion method based on convolutional neural network (CNN) is proposed. The proposed method incorporates the ideas of both hyper-sharpening and MS pan-sharpening techniques, thereby employing a two-stage cascaded CNN to reconstruct the anticipated high-resolution HS image. Technically, the proposed CNN architecture consists of two sub-networks, the detail injection sub-network and unmixing sub-network. The former aims at producing a latent high-resolution MS image, whereas the latter estimates the desired high-resolution abundance maps by exploring the spatial and spectral information of both HS and MS images. Moreover, two model-training fashions are presented in this paper for the sake of effectively training our network. Experiments on simulated and real remote sensing data demonstrate that the proposed method can improve the spatial resolution and spectral fidelity of HS image, and achieve better performance than some state-of-the-art HS pan-sharpening algorithms. [ABSTRACT FROM AUTHOR]

The papers in this special issue contain both the ?Mini-Special Issue on the 2014 IEEE Microwave Theory and Techniques Society (IEEE MTT-S) International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-Bio)? and the ?Mini-Special Issue on the 2015 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS).? [ABSTRACT FROM PUBLISHER]

In this paper, we design an Anomaly Detection (AD) framework for mobile data traffic, capable of identifying different types of anomalous events generated by flash crowds in metropolitan areas. We state the problem using a semi-supervised approach and exploit the great performance of different Recurrent Neural Network (RNN) models to learn the temporal context of input sequences. Our proposal processes real traffic traces from the unencrypted LTE Physical Downlink Control Channel (PDCCH) of an operative network, gathered during an extensive measurement campaign in two major cities in Spain. The AD framework is designed to perform: i) a-posteriori analysis to understand users’ behavior and urban environment variations; ii) real-time analysis to automatically and on-the-fly alert urban anomalies; and iii) estimation of the duration of the periods identified as anomalous. Numerical results show the higher performance of our AD framework compared to classic AD algorithms and confirm that the proposed framework predicts anomalous behaviours with high accuracy and regardless of their cause. [ABSTRACT FROM AUTHOR]

The knowledge of the water vapor (WV) distribution in the Earth’s atmosphere is of great importance for weather prediction. Meteorological models, in particular, the so-called limited area models, can assimilate humidity measurements, increasing the reliability of the simulated atmospheric dynamics. An important improvement can be achieved, for instance, if we are able to provide the total column with a sufficient precision and accuracy. In this paper, the novel normalized differential spectral attenuation (NDSA) approach is applied to retrieve the vertical profile of WV—and thus the total column—from measurements of differential attenuation signals at microwaves. A forward model (FM) has been used to simulate the ray-tracing of a microwave signal from a transmitter to a receiver in the atmosphere by using the 3-D atmospheric parameters as provided by a numerical weather prediction (NWP) model. From the NDSA measurement, the integrated WV (IWV) content can be directly derived. A further retrieval code is able to invert the measurements of IWV along the path length, providing the vertical humidity profile, which is directly related to the total vertical column assimilated by weather prediction models. In this paper, we show that the values of the total column can be retrieved with a precision and accuracy up to about 0.6% and 2.1%, respectively, which could have a positive impact on NWP models at short time scale. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (SAR) data are inevitably contaminated by polarization crosstalk and channel imbalance, which propagate to the error of final remote sensing product. To ensure the successful estimation of forest heights from forthcoming polarimetric SAR interferometry (PolinSAR) campaigns, a critical study on the polarimetric system requirements of PolinSAR for forest height mapping must be carried out. This paper establishes an analytical model for forest height estimation error including dependences on polarimetric system parameters including crosstalk, channel imbalance, and system noise. Simulation analyses are conducted on the real airborne SAR data acquired by the E-SAR system to validate the proposed theoretical error dependence model. We demonstrate that the established error model can be used not only by the system designers as a guidance for setting the polarimetric system requirements of PolinSAR for forest height mapping, but also by the data analyst to correct for systematic bias in the forest height inversion. [ABSTRACT FROM AUTHOR]

Dense stereo image matching in remotely sensed images is a challenging problem, though it has been studied for more than two decades, due to occlusions, discontinuities, geometric, and radiometric distortions. A novel multiobjective fitness function-based dense stereo matching approach using genetic algorithms (GAs) is proposed in this paper. The proposed method is useful for estimating dense disparity map with an improved number of inliers for a stereo image pair, despite the constraint of finding correct disparity at depth discontinuities. In this paper, the steps of GA, such as initialization of the population, fitness function, and crossover and mutation operation, are designed and implemented to effectively deal with the problem of dense stereo image matching. To initialize the population, a Scale Invariant Feature Transform (SIFT) descriptor is computed for each pixel and multiple-size window-based matching is performed, using the similarity measures: 1) Euclidean distance and 2) spectral angle mapper. The generated disparity maps are pruned to choose a suitable subset using the designed fitness functions, considering the constraints related to stereo image pair, such as epipolar constraint, which encodes the epipolar geometry and the similarity measure that is useful to decide accuracy of the correspondences. The two objective functions are the number of inliers computed using the fundamental matrix and an energy minimization function, considering discontinuities and occlusions. The usefulness of this approach for remotely sensed stereo image pairs is demonstrated by improving the number of inliers and favorably comparing with state-of-the-art dense stereo image matching methods. [ABSTRACT FROM AUTHOR]

Remotely sensed sea-surface temperature (SST) retrievals with a significant positive bias during the occurrence of upwelling phenomena along the southeastern coast of Brazil were reported in our companion paper. As a result, this paper proposes an automated bias correction algorithm to improve the MODIS long-wave multichannel SST (MCSST) retrievals during the abovementioned conditions in this region. In this paper, MODIS daytime SST data (SSTMODIS) and differences between brightness temperatures in MODIS channels 31 and 32 (BT31 − BT32) are analyzed simultaneously with hourly wind surface conditions, in situ SST at 0.3 and 10 m in depth (SSTbuoy03 and SSTbuoy10), and sensible and latent heat fluxes from the Cabo Frio buoy data (at 23° S, 42° W) during 2014. The obtained results show that some upwelling events present air temperature ($T_{\mathrm {air}}$) greater than SSTbuoy03 and low-atmospheric water vapor content. A simultaneous occurrence of these factors during upwelling conditions may lead to a warm-skin layer effect and may cause BT31 to be greater than SSTbuoy03 and BT31 − BT32 to be small (−0.18 °C ± 0.22 °C), affecting the MCSST performance. The proposed bias correction algorithm uses a least-squares curve between SSTbuoy03 and SSTMODIS retrievals when BT31 − BT32 ≤ 0.5 °C (i.e., dry atmospheric conditions). The bias correction algorithm has significantly improved the SSTMODIS bias (RMSE) from 1.43 °C to −0.2 °C (1.60 °C to 0.58 °C) when applied to 22 cloud-free pixels of MODIS during January–March of 2015. [ABSTRACT FROM AUTHOR]

Monitoring water quality with classical methods is not an easy task. Remote sensing with wide coverage and multiple temporal monitoring is the best solution for surface water quality monitoring. This paper demonstrates the determination of surface water quality parameters by using principal component analysis (PCA) data fusion and mining techniques with the aid of Landsat 8 OLI (L8 OLI), Sentinel 2A (S2A), and Göktürk-2 (GK2) satellite sensors. Chlorophyll-a, dissolved oxygen, total suspended solids, Secchi disk depth, total dissolved substance, and pH were the parameters selected for surface water quality analysis. High spectral resolution of L8 OLI/S2A images and the high spatial resolution of GK2 images were fused and analyzed by a suite of data mining models to provide more reliable images with both high spatial and temporal resolutions. Surface water quality parameters calculated by PCA-based response surface regression (RSR) method were compared with results obtained from multiple linear regression (MLR), artificial neural network (ANN), and support vector machines (SVMs) data mining methods. The performance of the data mining models derived using only multispectral band data and PCA fused data were quantified using four statistical indices; such as mean-square error (MSE), root MSE, mean absolute error, and coefficient of determination (R2). The analysis confirmed that the PCA-based RSR method is superior to MLR, ANN, and SVM data mining models to accurately estimate water quality parameters in lakes. [ABSTRACT FROM AUTHOR]

Spectrum access and interference management are amongst the most challenging issues in device-to-device (D2D) cellular networks. In order to address these issues, this paper introduces spatial spectrum sensing (SSS) for D2D cellular networks to facilitate cellular spectrum sharing by D2D users while providing a quality of service guarantee for cellular users. In order to assess the performance of the proposed scheme, we adopt a stochastic geometry approach in which the locations of base stations and D2D devices are modeled as independent Poisson point processes (PPPs). Assuming that the locations of the active cellular transmitters form another independent PPP, we characterize the area spectral efficiency of D2D networks under cellular users’ outage probability constraint. The use of SSS prohibits D2D transmissions around the active cellular users because of which the locations of the active D2D transmitters are modeled as a Poisson hole process driven by the PPP of active cellular user locations. Our analysis carefully accounts for this spatial separation between active cellular users and active D2D devices. Extensive simulation and numerical results are presented to verify our analysis and demonstrate the advantages of SSS-based D2D cellular networks. [ABSTRACT FROM AUTHOR]

The Fourier-based image correlation technique has been widely concerned due to its accuracy, efficiency, and robustness to image contrast and brightness. Accordingly, a variety of subpixel methods have been proposed. However, the detailed subpixel-level influence of the complicated radiometric variations has yet to be investigated, and few corresponding improvements have been made. This paper presents a novel illumination-robust subpixel Fourier-based image correlation method based on phase congruency. Both the magnitude and orientation information of the phase congruency features are adopted to construct a structural image representation. The image representation is then embedded into the correlation scheme of the subpixel methods, either by linear phase estimation in the frequency domain or by kernel fitting in the spatial domain, achieving two improved subpixel methods. The proposed methods integrate the advantages of the structural image representation and the original correlation scheme, and make full use of both global and local phase information to achieve illumination-robust correlation. Experiments undertaken with both simulated and real radiometric differences were carried out with ground-truth subpixel shifts. The performances of the proposed methods and the other state-of-the-art subpixel Fourier-based correlation methods were evaluated and compared. The experimental results indicate that the proposed methods outperform the other methods in the presence of diverse radiometric variations, in both accuracy and robustness. [ABSTRACT FROM AUTHOR]

This paper is devoted to solving a full-wave inverse scattering problem (ISP), which is aimed at retrieving permittivities of dielectric scatterers from the knowledge of measured scattering data. ISPs are highly nonlinear due to multiple scattering, and iterative algorithms with regularizations are often used to solve such problems. However, they are associated with heavy computational cost, and consequently, they are often time-consuming. This paper proposes the convolutional neural network (CNN) technique to solve full-wave ISPs. We introduce and compare three training schemes based on U-Net CNN, including direct inversion, backpropagation, and dominant current schemes (DCS). Several representative tests are carried out, including both synthetic and experimental data, to evaluate the performances of the proposed methods. It is demonstrated that the proposed DCS outperforms the other two schemes in terms of accuracy and is able to solve typical ISPs quickly within 1 s. The proposed deep-learning inversion scheme is promising in providing quantitative images in real time. [ABSTRACT FROM AUTHOR]

This paper presents a wireless sensor network system (WSNS) for effective, reliable, and efficient monitoring of landslides. The system incorporates a network of wireless inertial measurement unit (IMU) sensor devices for collecting movement data, a local base station for data gathering, a capture server for data processing and storage, and a warning system. The major contributions of this paper are: 1) two approaches for defining movement thresholds; 2) landslide classification concept based on IMU sensor data patterns and magnitudes; and 3) a conceptual framework for building an intelligent and reliable wireless monitoring and warning system. The IMU sensor data collected by three-axis accelerometer and three-axis gyroscope were utilized to define the movement thresholds and classify landslides based on specially designed laboratory experiments. The performances of the IMU sensors and the base station for data collection and communication were tested through a rock-fall experiment conducted in the field conditions. The WSN-IMU system is capable of monitoring all types of slope movements independent of the triggering factors. The unique ability of the WSN-IMU system to determine landslide types allows designers and authorized personnel to predict subsequent movement pattern and duration so as to implement appropriate risk management and control measures to alleviate the socioeconomic losses. This paper outcome serves as the foundation for future studies and technological advancements that will facilitate landslide stabilization or mitigation actions as well as to predict the intensity of damages associated with those landslides. [ABSTRACT FROM AUTHOR]

The data available in the world come in various modalities, such as audio, text, image, and video. Each data modality has different statistical properties. Understanding each modality, individually, and the relationship between the modalities is vital for a better understanding of the environment surrounding us. Multimodal learning models allow us to process and extract useful information from multimodal sources. For instance, image captioning and text-to-image synthesis are examples of multimodal learning, which require mapping between texts and images. In this paper, we introduce a research area that has never been explored by the remote sensing community, namely the synthesis of remote sensing images from text descriptions. More specifically, in this paper, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, to generate equivalent remote sensing images. From a methodological perspective, we propose to rely on generative adversarial networks (GANs) to convert the text descriptions into equivalent pixel values. GANs are a recently proposed class of generative models that formulate learning the distribution of a given dataset as an adversarial competition between two networks. The learned distribution is represented using the weights of a deep neural network and can be used to generate more samples. To fulfill the purpose of this paper, we collected satellite images and ancient texts to train the network. We present the interesting results obtained and propose various future research paths that we believe are important to further develop this new research area. [ABSTRACT FROM AUTHOR]

A method for remote wattmeter (RW) adjustment is introduced in this paper. The estimated adjustment gain is intended for corrections of wattmeter’s indications. Currently, legal metrology regulations do not allow such type of adjustment of certified instruments in the field. However, the proposed adjustment could be applicable in the areas of private or industrial subaccounting, home automation, and so on. If the achievable uncertainty could be specified, the method might be suggested for the wattmeter’s calibration or verification. The implementation of remote adjustment becomes possible if both remote and reference wattmeters are connected to the same electrical grid and are equipped with a data communication channel. The main idea of the method is based on a temporary injection of the additional load at the location of remote device and acquisition of synchronized power readings by both reference and remote devices. The acquired readings are then processed to derive an adjustment gain of the RW. The proposed method aims to cancel an influence of the measurement unit propagating medium between the reference and remote devices. It is assumed that the characteristics of the medium are constant during the short period that is necessary to acquire readings. It is demonstrated that using the simulation of the proposed setup in the case of typical power losses in distribution lines, the adjustment gain can be estimated precisely. It is shown that in the case of remote measurement, the injected load power uncertainty can be reduced by averaging the acquired power samples. [ABSTRACT FROM AUTHOR]

Snow albedo plays an important role in the global climate system. There are notable missing data and error uncertainties in the current remote sensing snow albedo products that are attributed to the limits of remote-sensing technology. Due to the uncertainties of meteorological factors and the differences in various forward model simulation methods, snow albedo forward simulations also have considerable uncertainties. This paper suggests a long-time-series reconstruction of snow albedo utilizing a forward radiation-transferring model and a remote-sensing retrieval model together with multisource remotely sensed data and meteorological data. The key to this paper is to estimate snow information for areas lacking data utilizing a forward model for snow albedo with clear physical mechanisms. The estimated snow information can be used as reliable data for snow albedo reconstructions. The results indicate that the long time series of snow albedo data obtained by coupling the snow albedo retrieval model and forward simulation model is highly accurate. The mean absolute error, root mean square error, Pearson’s correlation coefficient (R), and Nash–Sutcliffe efficiency coefficient of the observed and reconstructed snow albedos are 0.11, 0.14, 0.79, and 0.69, respectively. The reconstructed snow albedo data are underestimated by only 11% relative to the in situ snow surface albedo measurements. In the alpine mountain regions, the proposed method has a simulation accuracy that is 6% greater than that of the MOD10A1 SAD. This paper provides an effective reconstruction solution that improves the accuracy of estimations of snow albedo and fills gaps in the data. [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]

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]

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]

Surface acoustic wave sensors find their application in a growing number of fields. This interest stems in particular from their passive nature and the possibility of remote interrogation. Still, the sensor package, due to its size, remains an obstacle for some applications. In this regard, packageless solutions are very promising. This paper describes the potential of the AlN/ZnO/LiNbO3 structure for packageless acoustic wave sensors. This structure, based on the waveguided acoustic wave principle, is studied numerically and experimentally. According to the COMSOL simulations, a wave, whose particle displacement is similar to a Rayleigh wave, is confined within the structure when the AlN film is thick enough. This result is confirmed by comprehensive experimental tests, thus proving the potential of this structure for packageless applications, notably temperature sensing. [ABSTRACT FROM AUTHOR]

Nonlinear transmission lines (NLTLs) have been studied for high-power RF generation with good prospects of applications in pulse radars for remote sensing (SARs) and disruption of communications in the battlefield, for instance. In this paper, two 30-section hybrid NLTLs built using nonlinear inductors and capacitors (2.2- and 10-nF barium titanate ceramic capacitors with 10- $\mu \text{H}$ ferrite bead inductors) will be described. For the test, the line is fed by a negative input pump pulse generated by a 1 kV discharge of a 0.75- $\mu \text{F}$ storage capacitor via a fast 50-ns switching system composed by an insulated gate bipolar transistor switch and its gate circuit driver. In the hybrid line tests, using 2.2-nF ceramic capacitors the maximum soliton generation packet obtained on the middle section had a frequency of the order of 33 MHz with voltage modulation depth (VMD) of around 700 V. For every single shot, approximately 10 RF cycles with small damping were noted. With the hybrid line using 10-nF ceramic capacitors the soliton generation obtained on the middle section reached a frequency of the order 10 MHz, and VMD of around 200 V. The main conclusion from this experiment is that hybrid lumped NLTLs may be used to achieve RF in megahertz range with higher VMD compared their counterparts (i.e., capacitive or inductive lines) because of their stronger nonlinearity with the use of both nonlinear elements. [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]

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]

The article presents several award recipients who were honored during the International Geoscience and Remote Sensing Symposium held on July 25, 2006 is Seoul, Korea. Richard Bamler was honored for his contributions to pattern recognition and data fusion in remote sensing. Gary G. Gimmestad of Georgia Institute of Technology for his contributions to atmospheric remote sensing technology. Furthermore, Kamal Sarabandi also received an award for his outstanding research related to remote sensing.

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]

Reports on the presentation of the 2002 IEEE Geoscience and Remote Sensing Society's awards at the 2002 International Geoscience and Remote Sensing Symposium on June 23, 2003. IEEE certificates and awards bestowed by the society to promote excellence in research and service, each year; Recipient of the Outstanding Service Award.

The article offers information related to submitting papers, submission of a manuscripts, and copyright for the authors of the periodical.

The article provides information on the periodical "Institute of Electrical and Electronics Engineers (IEEE) Transactions on Geoscience and Remote Sensing" (TGRS). Topics discussed include the emphasis of the publication on science and engineering theory, concepts and techniques for sensing the land, oceans, atmosphere and space, need for a portable document format (pdf) version of all paper submitted to the periodical using the IEEE Standard format, and copyright policy of the IEEE.

This paper presents an opportunity to cancel range ambiguities in synthetic aperture radar (SAR) configuration. One of the limitations of SAR systems is the range ambiguity phenomenon that appears with long delayed echoes. The reflected signal corresponding to one pulse is detected when the radar has already transmitted the next pulse. Thus, this signal is considered as an echo from the next pulse. This paper investigates the opportunity of coding the transmitted pulses using an orthogonal frequency-division multiplexing pulse. The results show that coded-OFDM signals outperform conventional chirp signal and make it possible to relax constraints placed upon the pulse repetition frequency. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an optimized Monte Carlo procedure with the variance reduction technique incorporated. The proposed method makes use of some auxiliary variables which embed the correlation information intrinsic in the iteration process; hence, the procedure can be systemized. The use of intrinsic information and the capability to systemize the procedure draw the essential difference of the proposed method with the control variable method in the Monte Carlo literature. The expression for the variance is derived, and an appropriate optimization problem is solved. The analysis of electromagnetic scattering from rough surfaces presents an ideal testbed for the proposed method. For 1-D surfaces considered in this paper, the simulation results have demonstrated that the proposed method can be three to five times faster than the conventional Monte Carlo procedure for the horizontal polarization and approximately two to four times faster for the vertical polarization. Moreover, it shows smoother angular patterns. Since the proposed method is quite general because of the following: 1) no specification is made about how the iterations should be carried out; hence, advanced techniques can be combined to offer the maximum efficiency, and 2) the quantity of interest needs not to be the scattering coefficient, problems such as those encountered in random medium may be treated using the proposed method. [ABSTRACT FROM AUTHOR]