This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name

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Reports on the GRS society journals and their impact factors.

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This article considers the tremendous potential for satellite remote sensing information delivered via mobile digital applications to improve decision making in emerging agricultural economies. Earth observations have been available for use in weather and other models to support decision making since the late 1970s, with the launch of the advanced very-high-resolution radiometer and the Landsat sensors

Recounts the career and contributions of Josa Manuel Bioucas-Dias.

Recounts the career and contributions of Jakob van Zyl.

As a result of the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand the contents. Hence, methodological research on the automatic understanding of UAV videos is of paramount importance (Figure 1). In this article, we introduce a novel problem of event recognition in unconstrained

The Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) has organized the annual Data Fusion Contest (DFC) since 2006. The contest aims to establish new benchmarks for scientific challenges in remote sensing image analysis by promoting the use of multimodal data, leveraging new sensors as well as big data, and facilitating the emergence

Satellite technology has revolutionized the way we acquire terrestrial surface data. Such remotely sensed terrestrial data have made it possible to generate digital elevation models (DEMs) at multiple spatial and temporal scales by exploiting stereographic, interferometric, radargrammetric, and lidar principles [1]-[10]. Schemes adapted to generate DEMs are hugely successful, and, as such, DEM generation

Reports on GRSS society Chapter activities in Brazil.

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Advertisement: TechRxiv is a free preprint server for unpublished research in electrical engineering, computer science, and related technology. Powered by IEEE, TechRxiv provides researchers across a broad range of fields the opportunity to share early results of their work ahead of formal peer review and publication. Researchers are asked to upload their unpublished research.

Oil is the most important resource in modern industry. However, with the continuous exploitation of oil and gas, disasters caused by ground deformation occur frequently. Among these disasters, the problem of land subsidence caused by oil field exploitation is the most serious. Land subsidence can cause damage to oil field production equipment and urban infrastructure. Therefore, the necessity and importance

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands), which can be used to accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to conventional techniques (the so-called

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

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The articles in this special section focus on inSAR (synthetic aperture radar interferometry) technology. This technology provides the unique ability to quantitatively map Earth’s elevation and surface deformation with high spatial resolution and precision. For this reason, it is used in many remote sensing applications (e.g., landslides, wetland water-level observation, and mining subsidence). Since

Synthetic aperture radar (SAR) has the unique ability to precisely measure slow surface motions, and this capability is widely used in many applications. Most of these methods were developed in the last two decades, while the increase of available SAR data in recent years has allowed the use of these approaches in various scientific and commercial projects. Based on our long experience and 16 years

Over the past two decades, the time-series interferometric synthetic aperture radar (InSAR) technique has been developed to estimate surface deformation parameters. Because of its ability to monitor large-scale deformation with millimeter accuracy, the time-series InSAR technique has been applied in many fields, such as urban infrastructure monitoring, mining subsidence, and landslides. The obtained

Ground-based differential interferometry synthetic aperture radar (GB-DInSAR) is a promising deformation measurement technology developed in the last 20 years. The GBDInSAR system is capable of sensing millimeter-scale deformations in the range of tens of meters to several kilometers from the target area in a continuous, all-weather environment. This article introduces in detail the basic principles

Ground surface deformation monitoring, parameter inversion, and forward prediction are essential to mining deformation mechanism interpretation and its related geohazard assessment. Interferometric synthetic aperture radar (InSAR) has the capability to measure surface deformation in a very wide area with high spatial resolution, low cost, and high efficiency, which traditional geodetic surveying techniques

Interferometric methods are drivers of the recent exponential growth in the use of synthetic aperture radar (SAR) for monitoring both natural and anthropogenic hazards. Since the first use of interferometric SAR (InSAR) in the late 1990s to detect deformations associated with earthquakes and volcanoes, important developments have improved sensor performance and data-processing capabilities for the

Since 2000, interferometric synthetic-aperture radar (InSAR) has been an effective tool to map 2D water-level changes beneath vegetated wetlands with a high spatial resolution and centimetric accuracy. In the last two decades, SAR images obtained from different wavelengths and polarization modes have been tested across various wetland systems, and InSAR-derived water-level-change maps have improved

Early warning systems (EWSs) to detect and monitor landslides are a great challenge. They are important due to the high cost of catastrophic landslides and are challenging because of the difficulty in identifying a diverse range of landslide-triggering factors. While there has been a very limited number of successes, recent advances in Earth observation (EO) from the ground, aircraft, and space have

Presents information on the 2020 IEEE GRSS Data Fusion Contest.

Moving into 2020 marks a number of milestones (if only based on its simplified numerology). The beginning of a new year and a new decade is always a good opportunity for retrospection, especially when progress has been made and is underway. The year 2019 marked both the last year that the IEEE Geoscience and Remote Sensing Society (GRSS) Women in GRSS (WinGRSS) would be a committee on its own and the

Reports on GRSS society chapter activities.

Reports on GRSS society chapter activities.

Presents information on the International INGARSS 2020 Conference.

The ZiYuan3 (ZY3) is a civilian stereo surveying and mapping satellite from China operating under the framework of the Earth resources satellite series, and its objective is to fulfill 1:50,000 mapping and update largerscale fundamental geographic information products. This article introduces the ZY3-03 satellite's mission and payload specifications as well as its data utilization and distribution

The data obtained with remote sensing sensors are processed to fuel studies related to the Earth's resources and environment. Sensor technologies being developed are increasing the diversity and capabilities of remote sensing data. However, these advancements are also multiplying the volume of information enormously, straining resources for processing the data. Meanwhile, the analysis and interpretation

Ripe with possibilities offered by deep-learning techniques and useful in applications related to remote sensing, computer vision, and robotics, 3D point cloud semantic segmentation (PCSS) and point cloud segmentation (PCS) are attracting increasing interest. This article summarizes available data sets and relevant studies on recent developments in PCSS and PCS.

Scarce in situ data in the western and central Tibetan Plateau (TP) hinder scientific research on physical process representation in climate models. Satellite remote sensing and climate models are effective data sources in complex topography and harsh environments, but they have not been effectively validated or improved for lack of multiscale observations matching their pixel or grid scales. Therefore

Gaining access to labeled reference data is one of the great challenges in supervised machine-learning endeavors. This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges, such as urbanization and climate change, using state-of-the-art machine-learning techniques. To meet these pressing needs, especially in urban research

One of the most important applications of synthetic aperture radar interferometry (SARIF) is making a geometrical plot of observed points on the ground surface. For that purpose, we derive the two points' distances using a radar principle. Mathematically, this problem is a two-unknown-variables problem. To solve, it we need only two equations. Due to limitations of the measured distances by radar,

Target classification and recognition (TCR) are important information-extraction techniques for high-resolution remote sensing images (HRIs). However, because methods with high accuracy usually have higher time complexity, the massive remote sensing image has brought great difficulties for realtime application. In this article, we propose a hybrid, heterogeneous parallel processing algorithm to improve

Signals of opportunity (SoOp) has the potential to offer cost-effective global remote sensing for land applications. Because of the complexity of SoOp scattering over land, comprehensive bistatic scattering models and simulators can help demonstrate its feasibility. To investigate this potential, we have developed a generalized, fully polarimetric forward model: the SoOp Coherent Bistatic Scattering

The three-year Space-Based Information Support for the Prevention and Recovery of Forest Fires Emergency in the Mediterranean Area (PREFER) project was devoted to creating a satellite-based service infrastructure capable of providing up-to-date information to support the preparedness, prevention, recovery, and reconstruction phases of the forest fires emergency cycle in the European Mediterranean region

Satellite imagery is increasingly employed for land-use analysis and planning. In this article, we examine the economic value of high-resolution (HR) satellite images as perceived by direct users. Drawing on a French spatial-data infrastructure (SDI), the direct users of which are mostly from public bodies, we used a contingent-valuation method to evaluate their willingness to pay (WTP) for satellite

Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. Registration is among the most fundamental operations to generate almost all data products from remotely sensed scenes. It is a challenging task due to inevitable geometric and radiometric distortions during the data acquisition process

Synthetic-aperture radar (SAR) systems are widely used for monitoring vegetation and forested environments. Those that offer large-scale coverage, short revisiting times, and an under-foliage wave-penetration capability have been broadly employed to extract information about the forest structure. Similarly, 3D forest structures that serve as important indicators of productivity and biomass levels can

Polarimetry synthetic aperture radar (SAR) interferometry (PolInSAR) is a promising technology for globalscale forest-height mapping. Many PolInSAR space missions have been proposed or are under development. To ensure the performance of forest-height estimation, a critical study of the required system and platform parameters for the planned mission must be carried out. This involves theoretical modeling

Cross-track synthetic aperture radar (SAR) interferometry is a powerful technique that analyzes the phase shift each pixel undergoes between acquisitions of the same scene with just a slight change of viewpoint. These phase shifts provide information about the topography and, when more than two acquisitions are available at different dates, about possible slow motions along the line of sight, related

Interferometric synthetic aperture radar (InSAR) is a powerful remote sensing tool that enhances information acquisition. During InSAR processing, the phase denoising of the interferogram is a mandatory step for topography mapping and deformation monitoring. During the past three decades, a large number of effective algorithms have been developed for efforts related to this topic. In this article,

Along-track interferometric (ATI) synthetic aperture radar (SAR) has proven a powerful tool for ground-moving target indication (GMTI). ATI-SAR systems use more than one antenna (at least two), mounted on a moving platform and displaced along the moving direction. Each SAR antenna provides a complex image of the same ground scene acquired with a very small time delay, depending on the separation (baseline)

The knowledge we gain from research in climate science depends on the generation, dissemination, and analysis of high-quality data. This work comprises technical practice as well as social practice, both of which are distinguished by their massive scale and global reach. As a result, the amount of data involved in climate research is growing at an unprecedented rate. Climate model intercomparison (CMIP)