4.6 Article

Hierarchical Spatial-Spectral Feature Extraction with Long Short Term Memory (LSTM) for Mineral Identification Using Hyperspectral Imagery

期刊

SENSORS
卷 20, 期 23, 页码 -

出版社

MDPI
DOI: 10.3390/s20236854

关键词

feature extraction; mineral identification; hyperspectral imagery; convolutional neural network (CNN); long short term memory (LSTM)

资金

  1. National Key Research and Development Program of China [2016YFB0500505, 2017YFC0602104]
  2. National Natural Science Foundation of China [61975004]
  3. High Resolution Remote Sensing Application Demonstration System of Land and Resources (phase II) [04-Y30B01-9001-18/20]

向作者/读者索取更多资源

Deep learning models are widely employed in hyperspectral image processing to integrate both spatial features and spectral features, but the correlations between them are rarely taken into consideration. However, in hyperspectral mineral identification, not only the spectral and spatial features of minerals need to be considered, but also the correlations between them are crucial to further promote identification accuracy. In this paper, we propose hierarchical spatial-spectral feature extraction with long short term memory (HSS-LSTM) to explore correlations between spatial features and spectral features and obtain hierarchical intrinsic features for mineral identification. In the proposed model, the fusion spatial-spectral feature is primarily extracted by stacking local spatial features obtained by a convolution neural network (CNN)-based model and spectral information together. To better exploit spatial features and spectral features, an LSTM-based model is proposed to capture correlations and obtain hierarchical features for accurate mineral identification. Specifically, the proposed model shares a uniform objective function, so that all the parameters in the network can be optimized in the meantime. Experimental results on the hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in the Nevada mining area show that HSS-LSTM achieves an overall accuracy of 94.70% and outperforms other commonly used identification methods.

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