Download Image Analysis, Classification, and Change Detection in by Morton J. Canty PDF
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 InformationTransformations Discrete Fourier rework Discrete Wavelet TransformPrincipal ComponentsMinimum Noise FractionSpatial CorrelationFilters, Kernels, and Fields Convolution Theorem. Read more...
Read or Download Image Analysis, Classification, and Change Detection in Remote Sensing : With Algorithms for ENVI/IDL, Second Edition PDF
Similar remote sensing & gis books
The Definitive quantity on state-of-the-art Exploratory research of huge Spatial and Spatiotemporal Databases because the booklet of the 1st variation of Geographic info Mining and data Discovery , new ideas for geographic information warehousing (GDW), spatial facts mining, and geovisualization (GVis) were constructed.
Bringing a clean new point of view to distant sensing, object-based picture research is a paradigm shift from the conventional pixel-based method. that includes quite a few sensible examples to supply figuring out of this new modus operandi, Multispectral photograph research utilizing the Object-Oriented Paradigm experiences the present photograph research equipment and demonstrates merits to enhance info extraction from imagery.
“Sky Alert! What occurs while Satellites Fail” explores for the 1st time what our sleek global will be like if we have been all at once to lose such a lot, if now not all, of our area resources. the writer demonstrates humankind’s dependence on house satellites and express what may perhaps take place to numerous points of our financial system, protection, and day-by-day lives in the event that they have been all at once destroyed.
Within the wake of the so-called info expertise revolution, many stakeholders from the private and non-private sectors (including electorate) have certainly grown conversant in the promise and value of spatial info infrastructures (SDI) for information entry, use, and sharing. reading the hindrances in addition to the techniques and mechanisms of integration and implementation, Spatial information Infrastructures in Context: North and South investigates the technological and the non-technological points of the common adoption of spatial info infrastructures.
- Governing the Nexus: Water, Soil and Waste Resources Considering Global Change
- GNSS Markets and Applications (GNSS Technology and Applications)
- Cold Region Atmospheric and Hydrologic Studies
- Environmental modelling: finding simplicity in complexity
- GIS : A Computer Science Perspective
- Governments and geographic information
Additional info for Image Analysis, Classification, and Change Detection in Remote Sensing : With Algorithms for ENVI/IDL, Second Edition
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.