A classification technique of civil objects by artificial neural networks using estimation of entropy on synthetic aperture radar images

Kvasnov, Anton V.; Shkodyrev, Viacheslav

doi:10.5194/jsss-10-127-2021

Cited by 5 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Having obtained the data, it became possible to develop a supervised learning algorithm [ 42 , 43 ]. The results showed that the “Fine tree” algorithm with Gini diversity index separability criterion with 68.1% of correct results was more effective for linearly polarized wave ( Figure 9 ).…”

Section: Supervised Learning For Polarimetric Recognitionmentioning

confidence: 99%

Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition

Kvasnov

2023

Sensors

Self Cite

View full text Add to dashboard Cite

This article deals with the analysis of backscattering at automatic target recognition (ATR) by ground-based radar located on rough terrain surfaces, using the properties of wave polarization. The purpose of the study is to examine and compare linear and circular polarized reflected waves, which can be described by decomposition theorems. Coherent decompositions (Pauli, Krogager, Cameron decomposition) are considered in the case of a rough terrain, for which the advantage of the Pauli decomposition has been shown. The article demonstrates an approach to the extraction of polarization signal backscattering data for two types of vehicles with different profiles. It is shown that the measurement results can be calibrated by a corner reflector that takes into account the properties of the ground surface, and further used for ATR based on supervised learning algorithms. The accuracy of object classification was 68.1% and 54.2% for the signal generated by linearly and elliptically polarized waves, respectively. Based on these results, we recommend using a linearly polarized wave as an object recognition mechanism. At the same time, any reflected depolarized wave significantly reshapes the structure due to the rotation of the object profile and the influence of a rough surface (vegetation fluctuations). This explains the low recognition accuracy in general.

show abstract

Section: Supervised Learning For Polarimetric Recognitionmentioning

confidence: 99%