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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 research results, promising to advance computational practices and technologies within the academic and industrial communities. SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Current and emerging applications of SAR data for earth observation, object detection and recognition, change detection, navigation, and interference mitigation are highlighted. Cutting edge methods, with particular emphasis on machine learning, are included. Contemporary deep learning models in object detection and recognition in SAR imagery with corresponding feature extraction and training schemes are considered. State-of-the-art neural network architectures in SAR-aided navigation are compared and discussed further. Advanced empirical and Machine Learning models in retrieving land and ocean information ? wind, wave, soil conditions, among others, are also included.
Synthetic Aperture Radar (SAR) Data Applications presents a diverse collection of state-of-the-art applications of SAR data. Its aim is to create a channel of communication of ideas on ongoing and evolving uses and tools employing Machine Learning, and especially Deep Learning, methods in a series of SAR data applications. This book comprises a variety of innovative ideas, original works, research results, and reviews from eminent researchers, spanning from target detection and navigation to land classification and interference mitigation.
Synthetic Aperture Radar (SAR) is a microwave remote sensing technology which was first conceived in the early 1950s. SAR technology has since seen rapid progress. Today, SAR systems are operated from elevated places on land, from manned and unmanned aircraft and spacecraft. SARs can provide images on a 24-h basis and in all kinds of weather and have the ability to penetrate clouds, fog, and, in some cases, leaves, snow, and sand. They generate maps and data describing features of the surface or reflective object. The advent of Machine Learning in the SAR community created new opportunities and facilitated tasks in SAR data analysis. Machine Learning tools offer an ingenuity to existing and new algorithms.
The first chapter, “End-to-End ATR Leveraging Deep Learning,” discuss the need for efficient and reliable automatic target recognition (ATR) algorithms that can ingest a SAR image, find all the objects of interest in the image, classify these objects, and output properties of the objects. Their chapter lays out the required steps in any approach for performing these functions and describes a suite of deep learning algorithms which perform this end-to-end SAR ATR.
End-to-End ATR Leveraging Deep Learning
Change Detection in SAR Images Using Deep Learning Methods
Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval
Synthetic Aperture Radar Image Based Navigation Using Siamese Neural Networks
A Comparison of Deep Neural Network Architectures in Aircraft Detection from SAR Imagery
Machine Learning Methods for SAR Interference Mitigation
Classification of SAR Images Using Compact Convolutional Neural Networks
Multi-Frequency Polarimetric SAR Data Analysis for Crop Type Classification Using Random Forest
Automatic Determination of Different Soil Types via Several Machine Learning Algorithms Employing Radarsat-2 SAR Image Polarization Coefficients
Ocean and Coastal Area Information Retrieval Using SAR Polarimetry