Signal Processing for Remote Sensing

Highlighting the data acquisitions segment of remote sensing, this book covers Normalized Hilbert Transfroms and Its Applications, Statistical Pattern Recognition, Neural Network-Based Infrasound Event Classifier, and also focusing on the Siesmic Data Decomposition and Kalman Filtering for Weak Signal Detection in Remote Sensing.

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Written by leaders in the field, Signal Processing for Remote Sensing explores the data acquisitions segment of remote sensing. Each chapter presents a major research result or the most up to date development of a topic. The book includes a chapter by Dr. Norden Huang, inventor of the Huang-Hilbert transform who, along with and Dr. Steven Long discusses the application of the transform to remote sensing problems. It also contains a chapter by Dr. Enders A. Robinson, who has made major contributions to seismic signal processing for over half a century, on the basic problem of constructing seismic images by ray tracing. With rapid technological advances in both sensor and processing technologies, a book can only capture the current process and result. However, the numerous mathematical techniques provided in this book have lasting value, giving it a useful role for many years to come. While the majority of remote sensing titles cover only image processing, this book focuses on the data acquisitions segment of remote sensing. Its uniquely specific and practical approach allows you to directly apply the knowledge in this book to your field of remote sensing applications.