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Lithological mapping based on fully convolutional network and multi-source geological data / Ziye Wang in Remote sensing, vol 13 n° 23 (December-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 23 (December-1 2021) . - n° 4860 Titre : Lithological mapping based on fully convolutional network and multi-source geological data Type de document : Article/Communication Auteurs : Ziye Wang, Auteur ; Renguang Zuo, Auteur ; Hao Liu, Auteur Année de publication : 2021 Article en page(s) : n° 4860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage profond [Termes IGN] carte géologique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données géologiques [Termes IGN] fusion de données multisource [Termes IGN] Himalaya [Termes IGN] lithologie [Termes IGN] segmentation sémantique Résumé : (auteur) Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping. Numéro de notice : A2021-878 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13234860 Date de publication en ligne : 30/11/2021 En ligne : https://doi.org/10.3390/rs13234860 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99146 [article]