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[An object-oriented remote sensing image segmentation approach based on edge detection].

Yu-Min Tan¹, Jian-Zhu Huai, Zhong-Shi Tang

¹School of Transportation Science & Engineering, Beihang University, Beijing 100191, China. tanym@buaa.edu.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu

|August 17, 2010

Summary

This summary is machine-generated.

This study introduces a novel object-oriented algorithm for high-resolution remote sensing image segmentation. The method effectively combines edge detection and region merging for improved landscape object discrimination.

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Area of Science:

Remote Sensing
Image Processing
Computer Vision

Context:

High-resolution satellite imagery presents challenges for accurate landscape object extraction.
Existing image segmentation algorithms vary in their effectiveness with edge and topological information.

Purpose:

To develop an object-oriented algorithm for remote sensing image segmentation that leverages both edge and topological information.
To improve the discrimination of landscape objects in high-resolution imagery.

Summary:

A novel two-phase algorithm is proposed, utilizing the Susan edge filter on Quickbird imagery to generate an edge map.
This edge map guides a region-based segmentation process, employing a quad tree for efficient information retrieval and a merging criterion integrating edge and region features.
The algorithm was tested on Quickbird images, with results quantitatively evaluated and compared to existing methods like ENVI Zoom Definiens.

Impact:

Demonstrates stable convergence and efficiency in segmenting high-resolution remote sensing imagery.
Offers a potentially more effective approach for landscape object extraction compared to current methods.
Provides a foundation for further advancements in automated image analysis for geospatial applications.