scholarly journals Hybrid Compact Polarimetric SAR for Environmental Monitoring with the RADARSAT Constellation Mission

2020 ◽  
Vol 12 (20) ◽  
pp. 3283
Author(s):  
Brian Brisco ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh

Canada’s successful space-based earth-observation (EO) radar program has earned widespread and expanding user acceptance following the launch of RADARSAT-1 in 1995. RADARSAT-2, launched in 2007, while providing data continuity for its predecessor’s imaging capabilities, added new polarimetric modes. Canada’s follow-up program, the RADARSAT Constellation Mission (RCM), launched in 2019, while providing continuity for its two predecessors, includes an innovative suite of polarimetric modes. In an effort to make polarimetry accessible to a wide range of operational users, RCM uses a new method called hybrid compact polarization (HCP). There are two essential elements to this approach: (1) transmit only one polarization, circular; and (2) receive two orthogonal polarizations, for which RCM uses H and V. This configuration overcomes the conventional dual and full polarimetric system limitations, which are lacking enough polarimetric information and having a small swath width, respectively. Thus, HCP data can be considered as dual-pol data, while the resulting polarimetric classifications of features in an observed scene are of comparable accuracy as those derived from the traditional fully polarimetric (FP) approach. At the same time, RCM’s HCP methodology is applicable to all imaging modes, including wide swath and ScanSAR, thus overcoming critical limitations of traditional imaging radar polarimetry for operational use. The primary image data products from an HCP radar are different from those of a traditional polarimetric radar. Because the HCP modes transmit circularly polarized signals, the data processing to extract polarimetric information requires different approaches than those used for conventional linearly polarized polarimetric data. Operational users, as well as researchers and students, are most likely to achieve disappointing results if they work with traditional polarimetric processing tools. New tools are required. Existing tutorials, older seminar notes, and reference papers are not sufficient, and if left unrevised, could succeed in discouraging further use of RCM polarimetric data. This paper is designed to provide an initial response to that need. A systematic review of studies that used HCP SAR data for environmental monitoring is also provided. Based on this review, HCP SAR data have been employed in oil spill monitoring, target detection, sea ice monitoring, agriculture, wetland classification, and other land cover applications.

Author(s):  
S.V. Borshch ◽  
◽  
R.M. Vil’fand ◽  
D.B. Kiktev ◽  
V.M. Khan ◽  
...  

The paper presents the summary and results of long-term and multi-faceted experience of international scientific and technical cooperation of Hydrometeorological Center of Russia in the field of hydrometeorology and environmental monitoring within the framework of WMO programs, which indicates its high efficiency in performing a wide range of works at a high scientific and technical level. Keywords: World Meteorological Organization, major WMO programs, representatives of Hydrometeorological Center of Russia in WMO


2020 ◽  
pp. 1-10
Author(s):  
Bryce J. Dietrich

Abstract Although previous scholars have used image data to answer important political science questions, less attention has been paid to video-based measures. In this study, I use motion detection to understand the extent to which members of Congress (MCs) literally cross the aisle, but motion detection can be used to study a wide range of political phenomena, like protests, political speeches, campaign events, or oral arguments. I find not only are Democrats and Republicans less willing to literally cross the aisle, but this behavior is also predictive of future party voting, even when previous party voting is included as a control. However, this is one of the many ways motion detection can be used by social scientists. In this way, the present study is not the end, but the beginning of an important new line of research in which video data is more actively used in social science research.


Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.


Author(s):  
Jinhui Li ◽  
Yifei Ji ◽  
Yongsheng Zhang ◽  
Qilei Zhang ◽  
Haifeng Huang ◽  
...  

Author(s):  
Jinhui Li ◽  
Yifei Ji ◽  
Yongsheng Zhang ◽  
Qilei Zhang ◽  
Haifeng Huang ◽  
...  

2021 ◽  
Vol 55 (2) ◽  
Author(s):  
Piotr Zięba ◽  
Agnieszka Sękara ◽  
Katarzyna Sułkowska-Ziaja ◽  
Bożena Muszyńska

Humans have used mushrooms from the beginning of their history. However, during the last few decades, the market demand for these fruiting bodies has increased significantly owing to the spread in the capabilities of culinary and pharmacological exploitation. Natural mushroom resources have become insufficient to meet the support needs. Therefore, traditional methods of extensive cultivation as well as modern technologies have been exploited to develop effective growing recommendations for dozens of economically important mushroom species. Mushrooms can decompose a wide range of organic materials, including organic waste. They play a fundamental role in nutrient cycling and exchange in the environment. The challenge is a proper substrate composition, including bio-fortified essential elements, and the application of growing conditions to enable a continuous supply of fruiting bodies of market quality and stabilized chemical composition. Many mushroom species are used for food preparation. Moreover, they are treated as functional foods, because they have health benefits beyond their nutritional value, and are used as natural medicines in many countries. Owing to the rapid development of mushroom farming, we reviewed the growing technologies used worldwide for mushroom species developed for food, processing, and pharmacological industries.


2021 ◽  
Vol 7 ◽  
pp. e571
Author(s):  
Nurdan Ayse Saran ◽  
Murat Saran ◽  
Fatih Nar

In the last decade, deep learning has been applied in a wide range of problems with tremendous success. This success mainly comes from large data availability, increased computational power, and theoretical improvements in the training phase. As the dataset grows, the real world is better represented, making it possible to develop a model that can generalize. However, creating a labeled dataset is expensive, time-consuming, and sometimes not likely in some domains if not challenging. Therefore, researchers proposed data augmentation methods to increase dataset size and variety by creating variations of the existing data. For image data, variations can be obtained by applying color or spatial transformations, only one or a combination. Such color transformations perform some linear or nonlinear operations in the entire image or in the patches to create variations of the original image. The current color-based augmentation methods are usually based on image processing methods that apply color transformations such as equalizing, solarizing, and posterizing. Nevertheless, these color-based data augmentation methods do not guarantee to create plausible variations of the image. This paper proposes a novel distribution-preserving data augmentation method that creates plausible image variations by shifting pixel colors to another point in the image color distribution. We achieved this by defining a regularized density decreasing direction to create paths from the original pixels’ color to the distribution tails. The proposed method provides superior performance compared to existing data augmentation methods which is shown using a transfer learning scenario on the UC Merced Land-use, Intel Image Classification, and Oxford-IIIT Pet datasets for classification and segmentation tasks.


2021 ◽  
Author(s):  
Guillaume Drouen ◽  
Daniel Schertzer ◽  
Ioulia Tchiguirinskaia

<p>As cities are put under greater pressure from the threat of impacts of climate change, in particular the risk of heavier rainfall and flooding, there is a growing need to establish a hierarchical form of resilience in which critical infrastructures can become sustainable. The main difficulty is that geophysics and urban dynamics are strongly nonlinear with an associated, extreme variability over a wide range of space-time scales.</p><p>The polarimetric X-band radar at the ENPC’s campus (East of Paris) introduced a paradigm change in the prospects of environmental monitoring in Ile-de France. The radar is operated since May 2015 and has several characteristics that makes it of central importance for the environmental monitoring of the region.</p><p>Based on the radar data and other scientific mesurement tools, the platform for greater Paris was developped in participative co-creation, and in scientific collaboration with the world leader industrial in water management. As the need for data accessibility, a fast and reliable infrastructure were major requirements from the scientific community, the platform was build as a cloud-based solution. It provides scientific weather specialists, as well as water manager,  a fast and steady platform accessible from their web browser on desktop and mobile displays.</p><p>It was developped using free and open sources librairies, it is rooted on an integrated suite of modular components based on an asynchronous event-driven JavaScript runtime environment. It includes a comprehensive and (real-time) accessible database and also provides tools to analyse historical data on different time and geographic scales around the greater Paris.</p><p>The Fresnel SaaS (Sofware as a Service) cloud-based platform is an example of nowadays IT tools to dynamically enhance urban resilience. Developments are still in progress, in constant request and feedback loops from the scientific and professional world.</p>


2021 ◽  
Author(s):  
Angelo Odetti ◽  
Federica Braga ◽  
Fabio Brunetti ◽  
Massimo Caccia ◽  
Simone Marini ◽  
...  

<p>The IT-HR InnovaMare project, led by the Croatian Chamber of Economy, puts together policy instruments and key players for development of innovative technologies for the sustainable development of the Adriatic Sea (https://www.italy-croatia.eu/web/innovamare). The project aims at enhancing the cross-border cooperation among research, public and private stakeholders through creation of a Digital Innovation Hub (DIH). The goal is to increase effectiveness of innovation in underwater robotics and sensors to achieve and maintain a healthy and productive Adriatic Sea, as one of the crucial and strategic societal challenges existing at the cross-border level. Within InnovaMare, CNR ISMAR and INM institutes and OGS, in cooperation with the University of Zagreb and other project partners, contribute to developing a solution to access and monitor extremely shallow water by means of portable, modular, reconfigurable and highly maneuverable robotic vehicles. The identified vehicle is SWAMP, an innovative highly modular catamaran ASV recently developed by CNR-INM. SWAMP is characterised by small size, low draft, new materials, azimuth propulsion system for shallow waters and modular WiFi-based hardware&software architecture. Two SWAMP vehicles will be enhanced with a series of kits, tools and sensors to perform a series of strategic actions in the environmental monitoring of the Venice Lagoon: <br>i) An air-cushion-system-kit will be designed and developed. The vehicle will become a side-wall air-cushion-vehicle with reduction of drag and increase in speed. This will also increase the payload with a reduction of draft. <br>ii) An intelligent winch kit with a communication cable for the management of underwater sensors and tools.<br>iii) A GPS-RTK kit for highly accurate positioning in the range of centimeters.<br>iv) An Autonomous programmable device for image acquisition and processing based on the Guard1 camera. This camera acquires images content and, by means of a supervised machine learning approach, recognises/classifies features such as fish, zooplankton, seabed, infrastructures. The system is conceived for autonomous monitoring activities extended in time in fixed or mobile platforms.<br>v) A Multibeam Echo-sounder (MBES) coupled with an IMU (for pitch-roll compensation). MBES data can be used, also coupled with Cameras Imagery, through image-detection techniques for reconstruction and comprehensive knowledge of underwater environment and infrastructures. Possible analyses in coastal areas are: seabed mapping also for cultural heritage, offshore structures and resources and monitoring of biodiversity, hydrocarbon, marine litter, pollution.<br>vi) An underwater Radiometer for multiple analysis: temporal dynamics of optical properties of water; temporal dynamics of water turbidity from water reflectance; submerged vegetation and water depth mapping in optically shallow water; produce reference data for validation of satellite data.<br>vii) Automatic Nutrient Analyzer for real-time nutrient monitoring. This sensor measures nitrate with high accuracy over a wide range of environmental conditions (including extremely turbid and high CDOM conditions), from blue-ocean nitraclines to storm runoff in rivers and streams. <br>The final result of this pilot action is the creation of an innovative prototype platform for sea environmental monitoring. This will be validated through the analysis of results and draw up of guidelines for the improvement of underwater conditions.</p>


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