Titlebook: Synthetic Aperture Radar (SAR) Data Applications; Maciej Rysz,Arsenios Tsokas,Panos M. Pardalos Book 2022 The Editor(s) (if applicable) an

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1931-6828 version methods in a series of SAR data applications.Current.This carefully curated volume presents an in-depth, state-of-the-art discussion on many applications of Synthetic Aperture Radar (SAR). Integrating interdisciplinary sciences, the book features novel ideas, quantitative methods, and resear

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Machine Learning Methods for SAR Interference Mitigation,interferences are analyzed in detail. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends is provided from the perspective of cognitive and deep learning frameworks.

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Ocean and Coastal Area Information Retrieval Using SAR Polarimetry,ed-value products in the framework of marine oil pollution is discussed by means of experiments of actual polSAR data. In Sect. 3, the ability of polSAR information to assist a continuous monitoring of coastal profiles for vulnerability analysis purposes is demonstrated through thought experiments. Brief conclusions are drawn in Sect. 4.

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End-to-End ATR Leveraging Deep Learning,O) images, SAR images are not easily interpreted and therefore have historically required a trained analyst to extract useful information from images. At the same time, the number of high-resolution SAR systems and the amount of data they generate are rapidly increasing, which has resulted in a shor

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GULP

Synthetic Aperture Radar Image Based Navigation Using Siamese Neural Networks,s a multitude of novel applications including the possibility of developing self-reliant navigational techniques for global positioning system denied settings. To this effect, the broader aim of this chapter is to utilize image data generated by SAR to determine the location of a given system that i

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A Comparison of Deep Neural Network Architectures in Aircraft Detection from SAR Imagery,onducted with their performance comparison, since different neural networks are designed and tested using different datasets and measured with different metrics. In this book chapter, we compare the performance of six popular deep neural networks for aircraft detection from SAR imagery, to verify th