![]() Here we focus on single-image super resolution (SISR), where sub-pixel-scale features are inferred based only on the information contained in a single original image, though several techniques exist that take advantage of video ( Baker and Kanade 1999 Huang et al. ( Nasrollahi and Moeslund 2014) provide an detailed review of classical image super-resolution techniques. It involves artificially increasing the resolution of an image beyond the resolution of the sensor used to capture the image. Image super resolution is a well-established area of research in the field of computer vision and image processing. The model is trained on 6 months of reflectivity observations from the Langley Hill, Washington, radar (KLGX), and we find that it substantially outperforms common interpolation schemes for 4× and 8× resolution increases based on several objective error and perceptual quality metrics. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. Conceptually, a neural network may be able to learn relations between large-scale precipitation features and the associated sub-pixel-scale variability and outperform interpolation schemes. Recently, significant progress has been made in single-image super resolution using convolutional neural networks. Typically, this is done using interpolation schemes, which estimate sub-grid-scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. In an era where the applications of RF sensing are multiplying by the day, this book serves as an easily accessible primer on the nuances of deep learning for radar applications.Super resolution involves synthetically increasing the resolution of gridded data beyond their native resolution. This book is also a valuable resource for both radar engineers seeking to learn more about deep learning, as well as computer scientists who are seeking to explore novel applications of machine learning. ![]() Further chapters focus on specific radar applications, which relate to DNN design for micro-Doppler analysis, SAR-based automatic target recognition, radar remote sensing, and emerging fields, such as data fusion and image reconstruction.Įdited by an acknowledged expert, and with contributions from an international team of authors, this book provides a solid introduction to the fundamentals of radar and machine learning, and then goes on to explore a range of technologies, applications and challenges in this developing field. Subsequently, the book summarizes radar-specific issues relating to the different domain representations in which radar data may be presented to DNNs and synthetic data generation for training dataset augmentation. The book begins with three introductory chapters on radar systems and phenomenology, machine learning principles, and optimization for training common deep neural network (DNN) architectures. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of. Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance.
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