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Deep learning based lithology classification using dual-frequency Pol-SAR data

Wenguang Wang, Xin Ren, Yan Zhang and Meng Li

Article (2018)

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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%.

Uncontrolled Keywords

polarimetric synthetic aperture radar ; dual-frequency pol-sar data ; stacked sparse autoencoder ; lithology classification ; identification

Subjects: 2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering
Department: Department of Electrical Engineering
Funders: National Natural Science Foundation of China
Grant number: 61771028
PolyPublie URL: https://publications.polymtl.ca/5165/
Journal Title: Applied Sciences (vol. 8, no. 9)
Publisher: MDPI
DOI: 10.3390/app8091513
Official URL: https://doi.org/10.3390/app8091513
Date Deposited: 24 Feb 2023 16:06
Last Modified: 17 May 2023 03:12
Cite in APA 7: Wang, W., Ren, X., Zhang, Y., & Li, M. (2018). Deep learning based lithology classification using dual-frequency Pol-SAR data. Applied Sciences, 8(9), 19 pages. https://doi.org/10.3390/app8091513


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