| Technology and Methodology |
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Land Cover Classification with SVM Based on NWFE and Texture Features |
| CUI Lin-lin1,2, LUO Yi1, BAO An-ming1 |
1. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
2. Graduate School, Chinese Academy of Sciences, Beijing 100049, China |
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Abstract Land cover classification based on remote sensing image is of significant importance to agriculture, forestry and environment monitoring. Algorithm of remote sensing information retrieval is always an important research topic in this field. This paper made an effort to combine the Nonparametric Weighted Feature Extraction (NWFE) and texture features with the Support Vector Machines (SVM) so as to achieve a higher classification precision. The combined approach was applied to land cover classification of the Manasi River oasis in Xinjiang in 2006, and was compared with approaches of SVM based on Principal Component Analysis (PCA) and texture features and based on original bands and texture features. The results show that the method of SVM combined with NWFE and texture features can capture not only the distribution of land cover but also the difference among land cover types. An overall classification accuracy of 89.17% is obtained, which is better than those of two other classification results.
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| Keywords
Remote sensing
Wangwu mountain
Xichengshan karst depression
Origin of karst
Aerolite striking
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Issue Date: 07 March 2012
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2012,
Vol. 24
