Using Spatial Statistics to Model the Spatial Structure of Data in Remote Sensing Change Detection: The Case of Urban Sprawl

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" Using Spatial Statistics to Model the Spatial Structure of Data in Remote Sensing Change Detection: The Case of Urban Sprawl "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	150572
Doc. No	:	ET22364
Main Entry	:	Jeffrey Scott Spiker
Title Proper	:	Using Spatial Statistics to Model the Spatial Structure of Data in Remote Sensing Change Detection: The Case of Urban Sprawl
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	Spatial structure in remotely sensed imagery is shown to impact the changedetection procedure. Methods to incorporate spatial statistics into correlationbasedand regression-based change detection that control for the spatial structurein this data are proposed. These procedures are presented by means of an urbanchange detection case study utilizing Landsat TM and Landsat ETM+ imagery inthe area to the west of the Baltimore, MD / Washington D.C. metropolitan areabetween 1989 and 2002.In the correlation-based analysis, the results show that the use of point-to-pointcorrelation is often inappropriate in the examination of spatial datasets. Inaddition, spatial datasets require techniques specifically designed to account forthe unique properties.-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL45570.pdf
Title and statement of responsibility and	:	Using Spatial Statistics to Model the Spatial Structure of Data in Remote Sensing Change Detection: The Case of Urban Sprawl [Thesis]