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Multispectral satellite image unders...

Unsalan, Cem.

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Multispectral satellite image understanding.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multispectral satellite image understanding./
Author:	Unsalan, Cem.
Description:	255 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0373.
Contained By:	Dissertation Abstracts International65-01B.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3119263

Multispectral satellite image understanding.
Unsalan, Cem.

Multispectral satellite image understanding. - 255 p.

Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0373.

Thesis (Ph.D.)--The Ohio State University, 2003.

A problem of major interest to regional planning organizations, disaster relief agencies, and the military is the identification and tracking of land development across large scale regions, and over time. We develop an autonomous image analysis system to understand land development, especially residential and urban building organizations from satellite images.Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Multispectral satellite image understanding.
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035 $a (UnM)AAI3119263
035 $a AAI3119263
040 $a UnM $c UnM
100 1 $a Unsalan, Cem. $3 1953596
245 1 0 $a Multispectral satellite image understanding.
300 $a 255 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0373.
500 $a Adviser: Kim L. Boyer.
502 $a Thesis (Ph.D.)--The Ohio State University, 2003.
520 $a A problem of major interest to regional planning organizations, disaster relief agencies, and the military is the identification and tracking of land development across large scale regions, and over time. We develop an autonomous image analysis system to understand land development, especially residential and urban building organizations from satellite images.
520 $a We introduce a set of measures based on straight lines to assess land development levels in high resolution satellite images. Urban areas exhibit a preponderance of straight line features. Rural areas produce line structures in more random spatial arrangements. We use this observation to perform an initial triage on the image to restrict the attention of subsequent, more computationally intensive analyses.
520 $a Vegetation indices have been used extensively to estimate the vegetation density from satellite and airborne images for many years. We use these as the multispectral information for classification and house and road extraction. We focus on the normalized difference vegetation index (NDVI) and introduce a statistical framework to analyze and extend it. Using the established statistical framework, we introduce new a group of shadow-water indices.
520 $a We then extend our straight line based measures by developing a synergistic approach that combines structural and multispectral information. In particular, the structural features serve as cue regions for multispectral features.
520 $a After the initial classification of regions, we introduce computationally more expensive but more precise graph theoretical measures over grayscale images to detect residential regions. The graphs are constructed using lines as vertices, while graph edges encode their spatial relationships. We introduce a set of measures based on various properties of the graph. These measures are monotonic with increasing structure (organization) in the image. We present a theoretical basis for the measures.
520 $a Having detected the residential regions, we introduce a novel system to detect houses and street networks in these. We extensively use the multispectral information and graph theory to extract houses and road networks.
520 $a We evaluated the performance of each step statistically and obtained very promising results. Especially, detection performances in house and street detection in residential regions is noteworthy. These results indicate the functionality of our satellite image understanding system.
590 $a School code: 0168.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Artificial Intelligence. $3 769149
690 $a 0544
690 $a 0800
710 2 0 $a The Ohio State University. $3 718944
773 0 $t Dissertation Abstracts International $g 65-01B.
790 1 0 $a Boyer, Kim L., $e advisor
790 $a 0168
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3119263