A new method for classification of high spatial resolution remotely sensed imagery based on fusion of shape and spectral information of pixels
YANG Qingshan1, ZHANG Hua2
1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;
2. School of Environment Science and Spatial Informatics, China University of Mining and Technology(Xuzhou), Xuzhou 221116, China
In the classification of high spatial resolution remotely sensed imagery,due to the presence of the same object with different spectra, the dependence only on spectral information for classification is not enough. To improve the accuracy of classification, the authors proposed a novel spatial features extraction method for classification of the HSRMI. Firstly, neighborhood pixels' spatial relationship was described and used to calculate and extract the pixel homogeneous regions (PHR). Then, based on the extracted PHR, the pixels' shape index features, including length-width ratio(LW) and area-perimeter ratio(PAI), were extracted. Lastly, the pixel shape index features were normalized and combined with the spectral information to perform classification by using SVM classification method. Two different areas' QuickBird images were used to test the performance of proposed method. The experimental results show that the proposed method has the highest performance compared with pixel shape index(PSI) and spectral information, and can improve the classification accuracy of high spatial resolution remotely sensed imagery.
杨青山, 张华. 融合像元形状和光谱信息的高分遥感图像分类新方法[J]. 国土资源遥感, 2016, 28(4): 64-70.
YANG Qingshan, ZHANG Hua. A new method for classification of high spatial resolution remotely sensed imagery based on fusion of shape and spectral information of pixels. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 64-70.
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2016, Vol. 28 
