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Urban features classification based on objects segmentation and hyperspectral characteristics |
| SUN Xiaofang |
| Department of Geography, Minjiang College, Fuzhou 350121, China |
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Abstract Urban features classification is based on hyperspectral characteristics and high-resolution image segmentation objects. After the removal of bad lines and Smile effect, FLAASH atmospheric correction and 155 Hyperion bands were used in this study. Spectrum feature was used to determine objects recognition suitable spectral resolution, and after Hyperion dimensional reduction, 21 wide-bands were generated. Utility wavelet fusion was performed, and IKONOS high-resolution objects were generated by multi-resolution segmentation. On the basis of hierarchical analysis classification method for segmentation objects, fuzzy membership function of the vegetation red edge effect and the water absorption characteristics in the near infrared were used to complete first level classification. The larger distance of 10 Hyperion bands was used as feature bands, and the second level classification was completed by standard nearest neighbor classification. 9 types of urban features were separated. The classification results are better than the maximum likelihood classification and spectral angle mapper.
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| Keywords
multi-scale
sparse decomposition
dictionary
remote sensing image
fusion
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Issue Date: 15 August 2017
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2017,
Vol. 29
