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

Author:

Cao Shixian1,Wu Wenyuan12ORCID,Wang Xinyu1,Xie Shanjuan12

Affiliation:

1. Institute of Remote Sensing and Earth Sciences, School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China

2. Zhejiang Provincial Key Laboratory of Urban Wetlands and Regional Change, Hangzhou Normal University, Hangzhou 311121, China

Abstract

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.

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Publisher

MDPI AG

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