Although there have been numerous books on wavelet applications to various scientific disciplines, this cutting-edge, practical book is the first to concentrate on wavelet applications to remote sensing and subsurface sensing from an engineer's point of view. The book introduces you to wavelet transform uses in a wide range of sensing technologies, demonstrates the usefulness of combining the wavelet transform with other signal processing tools to solve complicated sensing technology problems, and features several time-saving algorithms and MATLAB codes that help you with your specific projects in the field. Supported with over 600 equations and more than 100 illustrations, this unique reference focuses on the processing of signals from Synthetic Aperture Radar (SAR). Specific remote sensing applications presented in the book include noise and clutter reduction in SAR images, SAR image compression, texture and boundary enhancement in SAR images, directional noise removal, and general image processing. You also find in-depth coverage of wavelet techniques for medical diagnostics from images. Other critical topics include artificial neural networks, the Markov random field, and artificial intelligence.
What this Book is About.; Wavelet Fundamentals - From Fourier Analysis to Wavelet Analysis. Continuous and Discrete Wavelet Transform. Multiwavelets. Wavelet Design.; Wavelet Algorithms and Associated Techniques - Discrete Wavelet Transform and Filter Bank Algorithm Based on MRA. Lifting Scheme for Discrete Wavelet Transform. Wavelet Packets: Symmetrical Tree Algorithm. Markov Random Field. Artificial Neural Networks. Anisotropic Diffusion.; Wavelet Applications to Processing of SAR Images - Speckle Noise Properties in Wavelet Domains. Noise Removal in SAR Images. ; SAR Image Compression - Choosing a Wavelet for SAR Image Compression. Wavelet-Based Tree-Structured Image Compression Methods. Compression of 3-D Remote Sensing Image in Three-Dimensional by SPIHT. Simultaneous Speckle Removal and Image Compression for SAR Image.; Detection and Classification Using Wavelets in Remote Sensing - Wayside Bearing Fault Detection. ; Wavelet Application to Screening Mammography - Detection of Malignant Masses. Detection of Microcalcification Clusters.;
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Andrew K. Chan
Andrew K. Chan is a professor of electromagnetics at Texas A&M University. Dr. Chan is a published author in major industry journals, including IEEE Transactions on Signal Processing and the International Journal of Numerical Modeling. He earned his Ph.D. at the University of Washington.
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Cheng Peng
Cheng Peng is currently with Texas Instruments Inc. He earned his Ph.D. in Electrical Engineering at Texas A&M University Aug. 2002. He published several papers in International Geoscience and Remote Sensing Symposium (IGARSS).