Abstract
Abstract. The article discusses the method for the classification of non-moving civil objects for information received from unmanned aerial vehicles (UAVs) by synthetic aperture radar (SAR). A theoretical approach to analysis of civil objects can be estimated by cross-entropy using a naive Bayesian classifier. The entropy of target spots on SAR images revaluates depending on the altitude and aspect angle of a UAV. The paper shows that classification of the target for three classes able to predict with fair accuracy P=0,964 based on an artificial neural network. The study of results reveals an advantage compared with other radar recognition methods for a criterion of the constant false-alarm rate (PCFAR<0.01). The reliability was confirmed by checking the initial data using principal component analysis.
Highlights
The trend of the modern airborne radar systems for ground monitoring is the introduction of machine learning and artificial intelligence technologies (Gini, 2008)
The physical principles of radar recognition are based on the received echo signals from radar contrast targets, Doppler shifts of moving objects, and changes in the polarization structure of the reflected wave (Lee and Pottier, 2009)
One of the prospective directions is the use of unmanned aerial vehicles (UAVs), which monitor the Earth’s surface by synthetic aperture radar (SAR)
Summary
The trend of the modern airborne radar systems for ground monitoring is the introduction of machine learning and artificial intelligence technologies (Gini, 2008). The physical principles of radar recognition are based on the received echo signals from radar contrast targets, Doppler shifts of moving objects, and changes in the polarization structure of the reflected wave (Lee and Pottier, 2009). One of the prospective directions is the use of unmanned aerial vehicles (UAVs), which monitor the Earth’s surface by synthetic aperture radar (SAR). These radar systems ensure images in real time are received at different altitudes and varying aspect angles (Moreira et al, 2013; Long et al, 2019). The potential accuracy can reach 0.3 m with a linear resolution using multilook processing in the spaceborne radar (Kim et al, 2014; Novak et al, 1998) (Fig. 1)
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