Classification of sediments on exposed tidal flats in the German Bight using multi-frequency radar data

2008 ◽  
Vol 112 (4) ◽  
pp. 1603-1613 ◽  
Author(s):  
M GADE ◽  
W ALPERS ◽  
C MELSHEIMER ◽  
G TANCK
Keyword(s):  
German Bight ◽  
Radar Data ◽  
Tidal Flats

scholarly journals Parametric Convolutional Neural Network for Radar-based Human Activity Classification Using Raw ADC Data

2020 ◽  
Author(s):  
Thomas Stadelmayer ◽  
Avik Santra
Keyword(s):  
Neural Network ◽  
Human Activity ◽  
Recognition System ◽  
Radar Data ◽  
Fast Time ◽  
Activity Classification ◽  
Radar Sensors ◽  
Equivalent State ◽  
Machine Interface

Radar sensors offer a promising and effective sensing modality for<br>human activity classification. Human activity classification enables several smart<br>homes applications for energy saving, human-machine interface for gesture<br>controlled appliances and elderly fall-motion recognition. Present radar-based<br>activity recognition system exploit micro-Doppler signature by generating Doppler<br>spectrograms or video of range-Doppler images (RDIs), followed by deep neural<br>network or machine learning for classification. Although, deep convolutional neural<br>networks (DCNN) have been shown to implicitly learn features from raw sensor<br>data in other fields, such as camera and speech, yet for the case of radar DCNN<br>preprocessing followed by feature image generation, such as video of RDI or<br>Doppler spectrogram, is required to develop a scalable and robust classification<br>or regression application. In this paper, we propose a parametric convolutional<br>neural network that mimics the radar preprocessing across fast-time and slow-time<br>radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for<br>classification of various human activities. It is demonstrated that our proposed<br>solution shows improved results compared to equivalent state-of-art DCNN solutions<br>that rely on Doppler spectrogram or video of RDIs as feature images.

scholarly journals Improved fuzzy logic method to distinguish between meteorological and non-meteorological echoes using C-band polarimetric radar data

2020 ◽  
Vol 13 (2) ◽  
pp. 537-551
Author(s):  
Shuai Zhang ◽  
Xingyou Huang ◽  
Jinzhong Min ◽  
Zhigang Chu ◽  
Xiaoran Zhuang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Radar Data ◽  
Weak Signal ◽  
Post Processing ◽  
Algorithm Performance ◽  
Radar Echoes ◽  
Fuzzy Logic Method ◽  
Anomalous Propagation ◽  
Logic Method

Abstract. To obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After a comprehensive analysis of the computational efficiency and radar system characteristics, we propose a fuzzy logic method that is similar to the MetSignal algorithm; the performance of this method is improved significantly in weak-signal regions where polarimetric variables are severely affected by noise. In addition, post-processing is adjusted to prevent anomalous propagation at a far range from being misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is incorporated into post-processing to suppress misclassification in the melting layer. An independent test set is selected to evaluate algorithm performance, and the statistical results show an improvement in the algorithm performance, especially with respect to the classification of meteorological echoes in weak-signal regions.

scholarly journals Regional Recognition and Classification of Active Loess Landslides Using Two-Dimensional Deformation Derived from Sentinel-1 Interferometric Radar Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1541
Author(s):  
Qingkai Meng ◽  
Pierluigi Confuorto ◽  
Ying Peng ◽  
Federico Raspini ◽  
Silvia Bianchini ◽  
...  
Keyword(s):  
Surface Deformation ◽  
Radar Data ◽  
Ground Movement ◽  
Tibet Plateau ◽  
Landslide Risk ◽  
Two Dimensional ◽  
Potential Threat ◽  
Loess Landslide ◽  
Deformation Velocity

Identification and classification of landslides is a preliminary and crucial work for landslide risk assessment and hazard mitigation. The exploitation of surface deformation velocity derived from satellite synthetic aperture radar interferometry (InSAR) is a consolidated and suitable procedure for the recognition of active landslides over wide areas. However, the calculated displacement velocity from InSAR is one-dimensional motion along the satellite line of sight (LOS), representing a major hurdle for landslide type and failure mechanism classification. In this paper, different velocity datasets derived from both ascending and descending Sentinel-1 data are employed to analyze the surface ground movement of the Huangshui region (Northwestern China). With global warming, precipitation in the Huangshui region, geologically belonging to the loess basin in the eastern edge of Qing-Tibet Plateau, has been increasing, often triggering a large number of landslides, posing a potential threat to local citizens and natural and anthropic environments. After processing both SAR data geometries, the surface motion was decomposed to obtain the two-dimensional displacements (vertical and horizontal E–W). Thus, a classification criterion of the loess landslide types and failure mode is proposed, according to the analysis of deformation direction, velocities, texture, and topographic characteristics. With the support of high-resolution images acquired by remote sensing and unmanned aerial vehicle (UAV), 14 translational slides, seven rotational slides, and 10 loess flows were recognized in the study area. The derived results may provide solid support for stakeholders to comprehend the hazard of unstable slopes and to undertake specific precautions for moderate and slow slope movements.

Classification Of Dual-polarisation Meteorological Radar Data

2005 ◽  
Author(s):  
L. Alparone ◽  
G. Benelli ◽  
A. Freni ◽  
D. Giuli ◽  
S. Minuti
Keyword(s):  
Radar Data ◽  
Meteorological Radar

scholarly journals Ocean forecasting for the German Bight: from regional to coastal scales

Ocean Science ◽  
2016 ◽  
Vol 12 (5) ◽  
pp. 1105-1136 ◽  
Author(s):  
Emil V. Stanev ◽  
Johannes Schulz-Stellenfleth ◽  
Joanna Staneva ◽  
Sebastian Grayek ◽  
Sebastian Grashorn ◽  
...  
Keyword(s):  
German Bight ◽  
Unstructured Grid ◽  
Wind Waves ◽  
Radar Data ◽  
Coastal Ocean ◽  
Hf Radar ◽  
Regional Models ◽  
Arctic Seas ◽  
The North ◽  
Ocean Forecasting

Abstract. This paper describes recent developments based on advances in coastal ocean forecasting in the fields of numerical modeling, data assimilation, and observational array design, exemplified by the Coastal Observing System for the North and Arctic Seas (COSYNA). The region of interest is the North and Baltic seas, and most of the coastal examples are for the German Bight. Several pre-operational applications are presented to demonstrate the outcome of using the best available science in coastal ocean predictions. The applications address the nonlinear behavior of the coastal ocean, which for the studied region is manifested by the tidal distortion and generation of shallow-water tides. Led by the motivation to maximize the benefits of the observations, this study focuses on the integration of observations and modeling using advanced statistical methods. Coastal and regional ocean forecasting systems do not operate in isolation but are linked, either weakly by using forcing data or interactively using two-way nesting or unstructured-grid models. Therefore, the problems of downscaling and upscaling are addressed, along with a discussion of the potential influence of the information from coastal observatories or coastal forecasting systems on the regional models. One example of coupling coarse-resolution regional models with a fine-resolution model interface in the area of straits connecting the North and Baltic seas using a two-way nesting method is presented. Illustrations from the assimilation of remote sensing, in situ and high-frequency (HF) radar data, the prediction of wind waves and storm surges, and possible applications to search and rescue operations are also presented. Concepts for seamless approaches to link coastal and regional forecasting systems are exemplified by the application of an unstructured-grid model for the Ems Estuary.

Classification of meteorological volumetric radar data using rough set methods

2003 ◽  
Vol 24 (6) ◽  
pp. 911-920 ◽  
Author(s):  
J.F. Peters ◽  
Z. Suraj ◽  
S. Shan ◽  
S. Ramanna ◽  
W. Pedrycz ◽  
...  
Keyword(s):  
Rough Set ◽  
Radar Data

Classification of Precipitation Types during Transitional Winter Weather Using the RUC Model and Polarimetric Radar Retrievals

2012 ◽  
Vol 51 (4) ◽  
pp. 763-779 ◽  
Author(s):  
Terry J. Schuur ◽  
Hyang-Suk Park ◽  
Alexander V. Ryzhkov ◽  
Heather D. Reeves
Keyword(s):  
Numerical Models ◽  
Radar Data ◽  
Storm Event ◽  
Model Output ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Precipitation Type ◽  
Microphysical Processes ◽  
Using Data

AbstractA new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma, the algorithm is tested on a transitional winter-storm event that produced a combination of rain, freezing rain, ice pellets, and snow as it passed over central Oklahoma on 30 November 2006. Examples are presented in which the presence of a radar bright band (suggesting an elevated warm layer) is observed immediately above a background classification of dry snow (suggesting the absence of an elevated warm layer in the model output). Overall, the results demonstrate the potential benefits of combining polarimetric radar data with thermodynamic information from numerical models, with model output providing widespread coverage and polarimetric radar data providing an observation-based modification of the derived precipitation type at closer ranges.

scholarly journals Polarimetric Synthetic Aperture Radar data for Crop Cover Classification

2014 ◽  
Vol II-8 ◽  
pp. 117-121 ◽  
Author(s):  
K. V. Ramana ◽  
P. Srikanth ◽  
U. Deepika ◽  
M. V. R. Sesha Sai
Keyword(s):  
Radar Data ◽  
Growth Stages ◽  
Polarization Data ◽  
Synthetic Aperture Radar Data ◽  
Amplitude Data ◽  
Crop Growth Stages ◽  
Single Polarization ◽  
Crop Discrimination ◽  
Microwave Sensors

The interest in crop inventory through the use of microwave sensors is on the rise owing to need for accurate crop forecast and the availability of multi polarization data. Till recently, the temporal amplitude data has been used for crop discrimination as well as acreage estimation. With the availability of dual and quadpol data, the differential response of crop geometry at various crop growth stages to various polarizations is being exploited for discrimination and classification of crops. An attempt has been made in the current study with RISAT1 and Radarsat2 C-band single, dual, fully and hybrid polarimetric data for crop inventory. The single date hybrid polarimetric data gave comparable results to the three date single polarization data as well as with the single date fully polarimetric data for crops like rice and cotton.

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