Hyperspectral Rock Classification Method Based on Spatial-Spectral Multidimensional Feature Fusion

被引:0
|
作者
Cao, Shixian [1 ]
Wu, Wenyuan [1 ,2 ]
Wang, Xinyu [1 ]
Xie, Shanjuan [1 ,2 ]
机构
[1] Hangzhou Normal Univ, Inst Remote Sensing & Earth Sci, Sch Informat Sci & Technol, Hangzhou 311121, Peoples R China
[2] Hangzhou Normal Univ, Zhejiang Prov Key Lab Urban Wetlands & Reg Change, Hangzhou 311121, Peoples R China
基金
中国国家自然科学基金;
关键词
hyperspectral imaging; rock image classification; convolutional neural network; recurrent neural network; space spectral fusion;
D O I
10.3390/min14090923
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The issues of the same material with different spectra and the same spectra for different materials pose challenges in hyperspectral rock classification. This paper proposes a multidimensional feature network based on 2-D convolutional neural networks (2-D CNNs) and recurrent neural networks (RNNs) for achieving deep combined extraction and fusion of spatial information, such as the rock shape and texture, with spectral information. Experiments are conducted on a hyperspectral rock image dataset obtained by scanning 81 common igneous and metamorphic rock samples using the HySpex hyperspectral sensor imaging system to validate the effectiveness of the proposed network model. The results show that the model achieved an overall classification accuracy of 97.925% and an average classification accuracy of 97.956% on this dataset, surpassing the performances of existing models in the field of rock classification.
引用
收藏
页数:18
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