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Multispectral satellite image understanding

Unsalan, Cem
http://rave.ohiolink.edu/etdc/view?acc_num=osu1061903845

Abstract Details

2003, Doctor of Philosophy, Ohio State University, Electrical Engineering.
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. 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. 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. 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. 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. 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. 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.
Kim Boyer (Advisor)
254 p.
land classification; house detection; road detection; building detection; computer vision

Recommended Citations

Citations

  • Unsalan, C. (2003). Multispectral satellite image understanding [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1061903845

    APA Style (7th edition)

  • Unsalan, Cem. Multispectral satellite image understanding. 2003. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1061903845.

    MLA Style (8th edition)

  • Unsalan, Cem. "Multispectral satellite image understanding." Doctoral dissertation, Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=osu1061903845

    Chicago Manual of Style (17th edition)

osu1061903845
2,853
© 2003, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.