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Interferometric synthetic aperture radar (InSAR) is a radar technique widely used in geodesy and remote sensing applications, e.g., topography reconstruction and subsidence estimation. Phase unwrapping (PU) is one of the key procedures of InSAR signal processing. Artificial intelligence (AI) techniques have proven to be potentially powerful in many fields and have been introduced into the PU domain

Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims to remove such noise so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970s, and several model-based algorithms

In past years, we have witnessed the fields of geosciences and remote sensing and artificial intelligence (AI) become closer. Thanks to the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have found common objectives and challenges to help advance the modeling and understanding of the Earth system. Despite such great opportunities, we have

We discuss best practices for developing multilanguage remote sensing training. Our discussion is based on the approach developed by the NASA-funded Applied Remote Sensing Training (ARSET) program for Spanish-speaking countries. Best practices include incorporating Spanish language at all phases of the training cycle, from outreach activities to program evaluation, and full integration of Spanish-speaking

In this era of environmental challenges, such as global climate change and local and regional air pollution, it is increasingly important that students develop skills to understand and address interdisciplinary scientific issues related to atmospheric studies. Involving students in optical remote sensing research projects is a high-impact educational strategy that can foster student interest and persistence

Reports on GRSS society summer schools in 2020.

Presents information on various GRSS Society chapters.

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these HS products, mainly by seasoned experts. However, with an ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges for reducing the burden of manual

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Presents a call for nominations for the GRSS Administrative Committee.

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Recounts the career and contributions of David Landgrebe.

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

The advent of lidar has revolutionized the way we observe and measure vegetation structure from the ground and from above and represents a major advance toward the quantification of 3D ecological observations. Developments in lidar hardware systems and data processing algorithms have greatly improved the accessibility and ease of use of lidar observations in ecological studies. A wide range of studies

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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.

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

Reports on GRSS society Chapter activities in Brazil.

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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

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.

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

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

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,

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

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

Following on the first part of our review of synthetic aperture radar (SAR) image statistical modeling [1], which concerns single-pixel statistical models, this article extends our discussion to spatial correlation analysis, focusing on SAR spatial correlation models and SAR clutter simulation methods. Two types of spatial correlation models, the product model and the coherent scatterer model, are

Pansharpening refers to the fusion of a multispectral (MS) image and panchromatic (PAN) data aimed at generating an outcome with the same spatial resolution of the PAN data and the spectral resolution of the MS image. In the last 30 years, several approaches to deal with this issue have been proposed. However, the reproducibility of these methods is often limited, making the comparison with the state

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.

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

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

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

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

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

This article represents the current state of the art in analyzing gas flaring (GF) from space. GF is a prominent source of air pollution, with significant global and local impacts. Its emissions contribute to climate change and air pollution, and its practice is a waste of a valuable energy source. A key barrier in evaluating the status of this phenomenon is the incomplete and inconsistent availability

With the rapid development of spaceborne synthetic aperture radar (SAR) technology and the acquisition of a large volume of SAR images, SAR image interpretation has become an urgent and difficult research topic. SAR image statistical modeling is one of the theoretical foundations for SAR image interpretation. It is of great value for the in-depth analysis of SAR images. This article reviews the major

Though widely studied over the years, radar target recognition is still far from being solved. Most earlier works rely on holistic features or raw intensity values, which are sensitive to the real-world sources of variability such as changes of pose, configuration, and imaging parameters; articulation; and occlusion. To solve these problems, this article resorts to the local descriptors around keypoints

The range of near-surface applications whereby the electrical and magnetic properties of the subsurface are investigated expands continually [1], and, particularly for terrestrial applications, increasingly higher spatial sampling densities are deployed [2]. Within the broad range of geophysical methods, small-loop frequency-domain electromagnetic (FDEM) instrumentation is often used to record variations

Reports on a student-oriented presentation at the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019) in Yokohama, Japan. Dr. Gail Skofronick-Jackson shared NASA satellite remote sensing examples at the conference. A program manager at NASA headquarters, Science Mission Directorate, Dr. Skofronick- Jackson provides scientific expertise and management support for the Research

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,

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