Mathematical principles of remote sensing : making inferences from noisy data / Andrew S. Milman.

Author
Milman, Andrew S. [Browse]
Format
Book
Language
English
Published/​Created
Chelsea, Mich. : Sleeping Bear Press, 1999.
Description
1 online resource (xiii, 406 pages) : illustrations

Details

Subject(s)
Summary note
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.
Notes
Bibliographic Level Mode of Issuance: Monograph
Bibliographic references
Includes bibliographical references and index.
Source of description
Description based on print version record.
Language note
English
Contents
  • 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
  • chapter C (t)=E[ƒ*(x)ƒ(x+t )].
  • chapter (10)
  • chapter (24)
  • chapter (31) P(? )= F(? ) , (32) (33) (34) (35) (36) P(? )=F*(? )G(? ).
  • chapter D(t )=E[f
  • chapter 9 6 Separating waves from noiselike features
  • chapter (77)
  • chapter (82)
  • chapter (94)
  • chapter (30)
  • chapter (34)
  • chapter (38)
  • chapter h(x)=0.
  • chapter (47)
  • chapter 10 7.1 Symmetric kernel
  • chapter 10 7.2 Asymmetric kernel
  • chapter (63)
  • chapter w=0.02 w=0.05 w=0.10 j ?j vj ?j vj ?j vj
  • chapter y=f(x), (6)
  • chapter u=y'+(1-a)u.
  • chapter 11 2.1 Logistic equation
  • chapter (26)
  • chapter 11 3.1 Outline of the iteration process
  • chapter (I -?AA)A =A
  • chapter =(aI+AA) A(aI+AA )(aI+AA)
  • chapter 11 4.2 Truncated iteration
  • chapter (73)
  • chapter (78)
  • chapter (83)
  • chapter (104)
  • chapter 0 < 1-aKK <1.
  • chapter (131)
  • chapter (138)
  • chapter 12 1 Matrix approach
  • chapter 12 1.3 Effects of noise
  • chapter (32)
  • chapter (61)
  • chapter (68)
  • chapter (75)
  • chapter 12 3 Discussion
  • chapter 13 1.1 Spatial filtering
  • chapter 13 3,1 Effects of noise
  • chapter (27) 13.4 A Backus -Gilbert approach
  • chapter 13 6 An integral-equation approach
  • chapter (65)
  • chapter (70)
  • chapter 14 1.1 An application of Lagrange multipliers
  • chapter (27)
  • chapter (52)
  • chapter (60)
  • chapter 14 5 Inequalities
  • chapter (85) 14.7 Law of large numbers and the central limit theorem
  • chapter (90)
  • chapter (a, f, g)=a(f, g) and (f, a, g)=a(f, g).
  • chapter (111)
  • chapter (114)
  • chapter 14 9 Orthogonal expansions
  • chapter 14 10 Orthonormalization
  • chapter (133)
  • chapter =f(y-cy-dy) +f(y-cy-dy) +f(y-cy-dy)
  • chapter b =a t . (143)
  • chapter 14 13 Proof by induction.
ISBN
  • 1-135-45760-3
  • 0-429-21997-0
  • 0-203-30578-7
  • 9786610290420
  • 1-280-29042-0
OCLC
  • 1000441262
  • 1027145524
Doi
  • 10.1201/b12790
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