A modified object-oriented classification algorithm and its application in high-resolution remote-sensing imagery |
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| Authors: | Zhong Chen Guoyou Wang Jianguo Liu |
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| Affiliation: | 1. Intelligence Control, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology , Wuhan , 430074 , PR China;2. State Key Laboratory for Multi-Spectral Information Processing Technology, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology , Wuhan , 430074 , PR China henpacked@163.com;4. State Key Laboratory for Multi-Spectral Information Processing Technology, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology , Wuhan , 430074 , PR China |
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| Abstract: | High-resolution satellite images offer abundant information on the Earth's surface for remote-sensing applications. The traditional pixel-based image classification method only used by spectral information has been proved to have several drawbacks. To satisfactorily interpret high-resolution imagery, other important information such as geometry, texture and semantics must be used, which are represented not only in single pixels but in meaningful image objects. So, a modified high-resolution image classification algorithm with multi-characteristics based on objects is presented in this article. First, image objects are extracted by multi-scale multi-characteristic segmentation. Second, characteristics such as spectral information, geometry, texture and semantics are extracted by the corresponding extraction algorithm. Finally, the image objects are classified by means of fuzzy-logic classification with a weighted average calculation method. Preliminary results show promise in terms of classification quality and accuracy. |
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