Wenguang Wang, Xin Ren, Yan Zhang et Meng Li
Article de revue (2018)
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Libre accès au plein texte de ce document Version officielle de l'éditeur Conditions d'utilisation: Creative Commons: Attribution (CC BY) Télécharger (1MB) |
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%.
Mots clés
polarimetric synthetic aperture radar ; dual-frequency pol-sar data ; stacked sparse autoencoder ; lithology classification ; identification
Sujet(s): | 2500 Génie électrique et électronique > 2500 Génie électrique et électronique |
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Département: | Département de génie électrique |
Organismes subventionnaires: | National Natural Science Foundation of China |
Numéro de subvention: | 61771028 |
URL de PolyPublie: | https://publications.polymtl.ca/5165/ |
Titre de la revue: | Applied Sciences (vol. 8, no 9) |
Maison d'édition: | MDPI |
DOI: | 10.3390/app8091513 |
URL officielle: | https://doi.org/10.3390/app8091513 |
Date du dépôt: | 24 févr. 2023 16:06 |
Dernière modification: | 06 avr. 2024 09:02 |
Citer en 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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