Mathematical principles of remote sensing: making inferences from noisy data

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.

Chapter 1 2A matrix equation -- chapter 1 4.2 Mean values -- chapter 1 5.2 Pieces of information -- chapter (16) -- chapter 1 8 Nonlinear systems -- chapter (29) -- chapter 2 1.2 Microwave radiation -- chapter (37) -- chapter 2 5 Line broadening -- chapter (13) -- chapter (17) -- chapter 5 2.1 Vector and matrix transpose -- chapter 5 2.3 Rank -- chapter (39) -- chapter UU=UU=I. -- chapter 5 2.7 Determinants -- chapter (57) -- chapter 5 3.1 Trace -- chapter 5 4.1 Nonsymmetric matrices -- chapter 5 5 Singular value decomposition -- chapter D=diag(s, s , ···, s ), -- chapter 5 7 Non-negative definite matrices -- chapter 5 8 Noise and the covariance matrix -- chapter (143) -- chapter 5 10 Finding eigenvectors -- chapter (14) -- chapter (20) -- chapter (21) -- chapter (42) -- chapter (51) -- chapter (62) -- chapter (69) -- chapter (74) -- chapter (81) -- chapter (85) -- chapter (91) -- chapter (96) -- chapter (105) -- chapter (109) -- chapter 8 1d-Functions -- chapter (31) -- chapter 8 2.7 Some useful functions -- chapter 8 3 Fourier series -- chapter (55) -- chapter 8 4 Discrete Fourier transform -- chapter (86) -- chapter (113) -- chapter 8 7.3Z-transform -- chapter References -- cha…