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Appl. Sci. 2018, 8(9), 1513; https://doi.org/10.3390/app8091513

Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

1
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2
Shouguang Vocational Center School, Shandong 262714, China
3
Electrical Engineering Department, Polytechnique Montreal, Montreal, QC H3C 3A7, Canada
*
Author to whom correspondence should be addressed.
Received: 17 July 2018 / Revised: 24 August 2018 / Accepted: 26 August 2018 / Published: 1 September 2018
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Abstract

Lithology classification is a crucial step in the prospecting process, and polarimetric synthetic aperture radar (Pol-SAR) imagery has been extensively used for it. However, despite significant improvements in both information content of Pol-SAR imagery and advanced classification approaches, lithology classification using Pol-SAR data may not provide satisfactory classification accuracy due to high similarity of certain classes. In this paper, a novel Pol-SAR lithology classification method based on a stacked sparse autoencoder (SSAE) is proposed. By using superpixel segmentation, new features can be extracted from dual-frequency Pol-SAR data, which can increase the class separability of the input data. Then, these features and the coherency matrices are incorporated into SSAE to classify the lithology. The classification performance is evaluated on an SIR-C dataset acquired over Xinjiang, China. The experimental result shows that this method is effective for lithology classification and can improve the overall accuracy up to 98.90%. View Full-Text
Keywords: polarimetric synthetic aperture radar; dual-frequency Pol-SAR data; stacked sparse autoencoder; lithology classification polarimetric synthetic aperture radar; dual-frequency Pol-SAR data; stacked sparse autoencoder; lithology classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, W.; Ren, X.; Zhang, Y.; Li, M. Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data. Appl. Sci. 2018, 8, 1513.

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