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

<p>The article discusses the method for the classification of non-moving group objects for information received from unmanned aerial vehicles (UAVs) by synthetic aperture radar (SAR). A theoretical approach to analysis of group 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 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>P</mi><mo>=</mo><mn mathvariant="normal">0</mn><mo>,</mo><mn mathvariant="normal">964</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="51pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="df81c30c4f71e9356f395d51472b8965"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="jsss-10-127-2021-ie00001.svg" width="51pt" height="11pt" src="jsss-10-127-2021-ie00001.png"/></svg:svg></span></span> 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 (<span class="inline-formula"><i>P</i>_CFAR&lt;0.01</span>). The reliability was confirmed by checking the initial data using principal component analysis.</p>

Abstract licence: CC BY

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