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.
孙小芳. 结合目标分割的高光谱城市地物分类[J]. 国土资源遥感, 2017, 29(3): 171-175.
SUN Xiaofang. Urban features classification based on objects segmentation and hyperspectral characteristics. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 171-175.
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2017, Vol. 29 
