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Proceedings Paper

Object-based algorithms and methods for quantifying urban growth pattern using sequential satellite images
Author(s): Bailang Yu; Hongxing Liu; Yige Gao; Jianping Wu
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Paper Abstract

Previously, urban growth pattern is described and measured by the pixel-by-pixel comparison of satellite images. The geographic extent, patterns and types of urban growth are derived from satellite images separated in time. However, the pixel-by-pixel comparison approach suffers from several drawbacks. Firstly, slight error in image geo-reference can cause false detection of changes. Secondly, it's difficult to recognize and correct artifact changes induced by data noise and data processing errors. Thirdly, only limited information can be derived. In this paper, we present a new objectbased method to describe and quantify urban growth patterns. The different types of land cover are classified from sequential satellite images as urban objects. The geometric and shape attributes of objects and the spatial relationship between them are employed to identify the different types of urban growth pattern. The algorithms involved in the object-based method are implemented by using C++ programming language and the software user interface is developed by using ArcObjects and VB.Net. A simulated example is given to demonstrate the utility and effectiveness of this new method.

Paper Details

Date Published: 30 August 2008
PDF: 11 pages
Proc. SPIE 7083, Remote Sensing and Modeling of Ecosystems for Sustainability V, 708305 (30 August 2008); doi: 10.1117/12.793369
Show Author Affiliations
Bailang Yu, East China Normal Univ. (China)
Texas A&M Univ. (United States)
Hongxing Liu, Texas A&M Univ. (United States)
Yige Gao, Texas A&M Univ. (United States)
Jianping Wu, East China Normal Univ. (China)

Published in SPIE Proceedings Vol. 7083:
Remote Sensing and Modeling of Ecosystems for Sustainability V
Wei Gao; Hao Wang, Editor(s)

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