Remote Sensing Gis

Download Image Analysis, Classification, and Change Detection in by Morton J. Canty PDF

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By Morton J. Canty

Photographs, Arrays, and MatricesMultispectral satellite tv for pc ImagesAlgebra of Vectors and MatricesEigenvalues and Eigenvectors Singular worth DecompositionVector Derivatives discovering Minima and Maxima snapshot data Random VariablesRandom Vectors Parameter EstimationHypothesis trying out and pattern Distribution FunctionsConditional chances, Bayes' Theorem, and category traditional Linear RegressionEntropy and Read more...


Covers such themes as simple Fourier transforms, wavelets, precept elements, minimal noise fraction transformation, and othorectification. This publication additionally discusses panchromatic sharpening, Read more...

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Read or Download Image Analysis, Classification, and Change Detection in Remote Sensing : With Algorithms for ENVI/IDL, Second Edition PDF

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Thus dx = |2y|, dy px (w(y)) = e−y , 2 and we obtain py (y) = 2ye−y , 2 y > 0. 14) where −∞ < μ < ∞ and σ 2 > 0. 10 that G = μ, var(G) = σ 2 (see Exercise 2). This is commonly abbreviated by writing G ∼ N (μ, σ 2 ). 15) where the standard normal density, φ(t), is given by 1 φ(t) = √ exp(−t2 /2). 16) Since it is not possible to express the normal distribution function, (g), in terms of simple analytical functions, it is tabulated. 17) so it is sufficient to give tables only for g ≥ 0. 18) so that values for any normally distributed random variable can be read from the table.

The probability of getting y misclassifications (and hence n − y correct classifications) in n trials in a specific sequence is θ y (1 − θ )n−y . In this expression, there is a factor θ for each of the y misclassifications and a factor (1 − θ ) for each of the n − y correct classifications. Taking the product is justified by the assumption that the trials are independent of each other. The number of such sequences is just the number of ways of selecting y trials from n possible ones. This is given by the binomial coefficient n!

This may be written in the form ¯ − s √σ ≤ μ < G ¯ + s √σ Pr G m m = 2 (s) − 1. 42) covers the unknown mean value, μ, is 2 (s) − 1. 42, then μ either lies within it or it does not. ization of G ∗ Generalized to a linear combination of n random variables. 43 Image Statistics Therefore, one can no longer properly speak of probabilities. Instead, a degree of confidence for the reported interval is conventionally given and expressed in terms of a (usually small) quantity α defined by 1 − α = 2 (s) − 1.

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