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- 619 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Signal and Image Processing for Remote Sensing
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About This Book
Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting info
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Yes, you can access Signal and Image Processing for Remote Sensing by C.H. Chen in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Engineering. We have over one million books available in our catalogue for you to explore.
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Table of contents
- Front Cover
- Contents
- Preface
- Editor
- Contributors
- Chapter 1: On the Normalized Hilbert Transform and Its Applications to Remote Sensing
- Chapter 2: Nyquist Pulse-Based Empirical Mode Decomposition and Its Application to Remote Sensing Problems
- Chapter 3: Hydroacoustic Signal Classification Using Support Vector Machines
- Chapter 4: Huygens Construction and the Doppler Effect in Remote Detection
- Chapter 5: Compressed Remote Sensing
- Chapter 6: Context-Dependent Classification : An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions
- Chapter 7: NMF and NTF for Sea Ice SAR Feature Extraction and Classification
- Chapter 8: Relating Time Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics
- Chapter 9: Use of a Prediction-Error Filter in Merging High- and Low-Resolution Images
- Chapter 10: Hyperspectral Microwave Atmospheric Sounding Using Neural Networks
- Chapter 11: Satellite Passive Millimeter-Wave Retrieval of Global Precipitation
- Chapter 12: On SAR Image Processing : From Focusing to Target Recognition
- Chapter 13: Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface
- Chapter 14: An ISAR Technique for Refocusing Moving Targets in SAR Images
- Chapter 15: Active Learning Methods in Classification of Remote Sensing Images
- Chapter 16: Crater Detection Based on Marked Point Processes
- Chapter 17: Probability Density Function Estimation for Classification of High-Resolution SAR Images
- Chapter 18: Random Forest Classification of Remote Sensing Data
- Chapter 19: Sparse Representation for Target Detection and Classification in Hyperspectral Imagery
- Chapter 20: Integration of Full and Mixed Pixel Techniques to Obtain Thematic Maps with a Refined Resolution
- Chapter 21: Signal Subspace Identification in Hyperspectral Imagery
- Chapter 22: Image Classification and Object Detection Using Spatial Contextual Constraints
- Chapter 23: Data Fusion for Remote-Sensing Applications
- Chapter 24: Image Fusion in Remote Sensing with the Steered Hermite Transform
- Chapter 25: Wavelet-Based Multi/Hyperspectral Image Restoration and Fusion
- Chapter 26: Land Cover Estimation with Satellite Image Using Neural Network
- Chapter 27: Twenty-Five Years of Pansharpening : A Critical Review and New Developments
- Back Cover