Mathematical Principles of Remote Sensing Making Inferences from Noisy Data

About this book

Mathematical Principles of Remote Sensing is an informative reference, or working textbook, on the mathematics, and general physical and chemical processes behind remote sensor measurements. The issues and mathematical principles important to remote sensing and data analysis are covered extensively, including measurements and noise, physics of electromagnetic radiation, and radiation transfer. Specific mathematical methods include covariance and probability analysis, regression, linear algebra, Fourier transforms, convolution, and others. This book is an essential reference for remote sensing scientists and engineers concerned with applications in radiation transfer, image processing, atmospheric and noise correction, and modelling.

Contents

1. Introduction Measurement and Noise. 2. Lights and Atoms. 3. Instruments and Noise. 4. Radiative Transfer. 5. Covariance Matrices. 6. Regression. 7. Matrix Solution of Linear Equations. 8. Fourier Transforms. 9. Autocorrelation Functions and Spectra. 10. Integral Equations. 11. Iteration. 12. Resolution and Noise. 13. Convolution and Images. 14. Mathematical Appendix. 3. Instruments and Noise. 4. Radiative Transfer. 5. Covariance Matrices. 6. Regression. 7. Matrix Solution of Linear Equations. 8. Fourier Transforms. 9. Autocorrelation Functions and Spectra. 10. Integral Equations. 11. Iteration. 12. Resolution and Noise. 13. Convolution and Images. 14. Mathematical Appendix.

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