首页|New Machine Learning Research from Remote Sensing Technology Institute Discussed (SAR Coherence Estimation by Composition of Subsample Estimates and Machine Lea rning)
New Machine Learning Research from Remote Sensing Technology Institute Discussed (SAR Coherence Estimation by Composition of Subsample Estimates and Machine Lea rning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting originating from Oberpfaffenhofen, Germany, by NewsRx correspondents, research stated, "Synthetic aperture radar ( SAR) coherence magnitude is an essential parameter in SAR interferometry." Our news reporters obtained a quote from the research from Remote Sensing Techno logy Institute: "This is the reason why current interferometric wide area ground motion services require the estimation of the coherence magnitude as accurately and computationAlly effectively as possible. The objective of this article is t o improve the accuracy of this coherence estimation compared to known estimators , especiAlly when estimating low coherences and working with a smAll, i.e., N $ <$ 30, but also large number of samples, i.e ., hundred or more. Precisely, this article proposes the interferometric coheren ce magnitude estimation by composition of subsample estimates and machine learni ng (ML). The principle is to partition the given sample and to estimate coherenc es on these independent subsamples using different coherence magnitude estimator s. It results in a nonparametric and automated statistical inference. It is show n that the composite ML estimator has a high estimation quality yet without prio r information, provides a deterministic estimate and is numericAlly efficient, i t is suitable for general interferometric synthetic aperture radar applications and operational systems."
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