Influence of Wave Polarization on the Texture Characteristics of Objects in Radar Images

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

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The research study in the convergence of the results of processing radar images Sentinel-1A with field observations of agricultural sites

Geodesy and Cartography ◽

10.22389/0016-7126-2019-950-8-52-58 ◽

2019 ◽

Vol 950 (8) ◽

pp. 52-58

Author(s):

D.V. Mozer ◽

Е.L. Levin ◽

A.K. Satbergenova

Keyword(s):

Satellite Remote Sensing ◽

Satellite Images ◽

Research Study ◽

High Accuracy ◽

Agricultural Fields ◽

Field Observations ◽

Radar Images ◽

The Earth ◽

Radar Satellite ◽

Modern Technologies

The manuscript discusses how to monitor the condition of seedlings on agricultural fields planted with winter wheat, fodder maize and areas of fir forest located in the Freudenstadt district of Baden-Wuerttemberg in Germany. To solve the range of agricultural problems , they often use modern technologies such as satellite remote sensing of the Earth. The paper displays the monitoring results of the Sentinel-1A radar satellites scenes, as well as visual spectrum imagery of field observations are presented when leaving directly to terrain segments. The processing deployed data chain, consisting of 11 Sentinel-1A scenes acquired in the timefrane from March to November 2018. Specifically, the SNAP Sentinel Toolboxes software was used to process the radar satellite images Sentinel-1А, the. Based on the the research outcomes the Committee of Agriculture of the Freudenstadt district is able to predict the yield amount with high accuracy due to good data convergence. According to the study, the following three important problems can be resolved by means of Sentinel-1A imagery

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Gaussian Process Regression for Estimating Wind Speed From X-band Marine Radar Images

OCEANS 2018 MTS/IEEE Charleston ◽

10.1109/oceans.2018.8604842 ◽

2018 ◽

Author(s):

Xinwei Chen ◽

Weimin Huang

Keyword(s):

Wind Speed ◽

Gaussian Process ◽

Gaussian Process Regression ◽

Radar Images ◽

X Band ◽

Marine Radar

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Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

Environmental Sciences Proceedings ◽

10.3390/iecg2020-07916 ◽

2020 ◽

Vol 5 (1) ◽

pp. 13

Author(s):

Negar Tavasoli ◽

Hossein Arefi

Keyword(s):

Machine Learning ◽

Carbon Stock ◽

Vegetation Indices ◽

Forest Biomass ◽

Principal Component ◽

Optical Data ◽

Above Ground Biomass ◽

Ground Biomass ◽

Texture Characteristics ◽

Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

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Progressive neighbors pursuit for radar images classification

Applied Soft Computing ◽

10.1016/j.asoc.2021.107194 ◽

2021 ◽

pp. 107194

Author(s):

Shuyuan Yang ◽

Guangying Xu ◽

Huixiao Meng ◽

Min Wang

Keyword(s):

Radar Images

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2D-wavelet based micro and macro texture analysis for asphalt pavement under snow or ice condition

Journal of Infrastructure Preservation and Resilience ◽

10.1186/s43065-021-00029-y ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Feng Li ◽

Gulnigar Ablat ◽

Siqi Zhou ◽

Yixin Liu ◽

Yufeng Bi ◽

...

Keyword(s):

Wavelet Transform ◽

Asphalt Pavement ◽

Asphalt Mixture ◽

Micro Scale ◽

Braking System ◽

Macro Scale ◽

Texture Characteristics ◽

The Mean ◽

Resistance Performance

AbstractIn ice and snow weather, the surface texture characteristics of asphalt pavement change, which will significantly affect the skid resistance performance of asphalt pavement. In this study, five asphalt mixture types of AC-5, AC-13, AC-16, SMA-13, SMA-16 were prepared under three conditions of the original state, ice and snow. In this paper, a 2D-wavelet transform approach is proposed to characterize the micro and macro texture of pavement. The Normalized Energy (NE) is proposed to describe the pavement texture quantitatively. Compared with the mean texture depth (MTD), NE has the advantages of full coverage, full automation and wide analytical scale. The results show that snow increases the micro-scale texture because of its fluffiness, while the formation of the ice sheets on the surface reduces the micro-scale texture. The filling effect of snow and ice reduces the macro-scale texture of the pavement surface. In a follow-up study, the 2D-wavelet transform approach can be applied to improve the intelligent driving braking system, which can provide pavement texture information for the safe braking strategy of driverless vehicles.

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