High resolution remote sensing image segmentation based on multi-features fusion

Authors

  • Yongqing Wang Department of Computer Science and Applications, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, China Henan aviation economics research center, collaborative innovation centers of aviation economic development & aeronautic materials technology, Zhengzhou 450015, China
  • Chunxiang Wang Basic Course Department, Henan Polytechnic, Zhengzhou 450046, China

Keywords:

multi-features fusion, high resolution, image segmentation, remote sensing, kernel clustering

Abstract

High resolution remote sensing images contain richer information of spatial relation in ground objects than low resolution ones, which can help to describe the geometric information and extract the essential features more efficiently. However, the handling difficulties due to the relative poorer spectral information, represented by phenomena of different objects with the same spectrum and the same object with the different spectrum, may cause the spectrum-based methods to fail. Besides, the inherent geometric growth in processing of traditional methods caused by growing pixels always leads to longer processing time, poorer precision, and lower efficiency. Combining the spectral features with textural and geometric features, we proposed a novel kernel clustering algorithm to segment high resolution remote sensing images. The experimental results were compared with mean shift and watershed algorithms, which validated the effectiveness and reliability of the proposed algorithm. 

Author Biography

Yongqing Wang, Department of Computer Science and Applications, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, China Henan aviation economics research center, collaborative innovation centers of aviation economic development & aeronautic materials technology, Zhengzhou 450015, China

Yongqing Wang, received his B.S.degree in fundamental mathematics from Henan Normal University, China, in June 2000, his M.S. degree in control theory and control engineering from Henan Normal University, China, in June 2003, and his Ph.D. degree in computer science at the Institute of Automation of the Chinese Academy of Sciences, in April 2009. He is a lecturer at the Department of Computer Science and Applications, Zhengzhou Institute of Aeronautical Industry Management, China. His current research interest includes machine learning and data mining.

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Published

2017-07-18

Issue

Engineering Review, Vol. 37, No. 3

Section

Articles

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