Statistical Models for Multiple-Scaled Analysis

Spatial variability often changes with respect to time and the characterization of this change is an important aspect of spatial-temporal modeling. Spatial variation is also important for classification analysis of remotely sensed data since pixels are grouped into classes because of similarities, a...

Full description

Saved in:
Bibliographic Details
Published inScale in Remote Sensing and GIS pp. 273 - 293
Main Author Myers, Donald E.
Format Book Chapter
LanguageEnglish
Published United Kingdom Routledge 1997
CRC Press LLC
Edition1
Subjects
Variogram Estimation
Minimized Error Variance
Spatial Data Set
Spatial Correlation
Covariance Function
AVHRR
Multiband Images
Spatial Variability
Regular Grid
Separation Vector
Conditional Expectation
Spatial Database Managers
Fractal Dimension
Common Data Model
Spatial Dependence
Indicator Transform
Pixel Values
Environmental Data Set
Radial Basis Function Interpolator
EMAP
Sample Variogram
Generalize PCA
True Confidence Intervals
Random Function
Point Values
Online AccessGet full text

Cover

More Information
Summary:Spatial variability often changes with respect to time and the characterization of this change is an important aspect of spatial-temporal modeling. Spatial variation is also important for classification analysis of remotely sensed data since pixels are grouped into classes because of similarities, and classes are distinguished because of dissimilarities. That is, it is necessary to identify and quantify within-band correlations but it is also necessary to quantify and characterize between-band correlation. The use of principal components analysis to remove noise from multiband images is a well-known technique and is based on the premise that the noise term corresponds to a relatively small part of the total variance. Within-pixel and between-pixel variation are related to the total within-image variation. This quantification may be considered for each layer separately or it may be determined for multiband images.
ISBN:9781566701044
156670104X
DOI:10.1201/9780203740170-14