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Addressing overfitting on point cloud classification using Atrous XCRF / Hasan Asy’ari Arief in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 90 - 101 Titre : Addressing overfitting on point cloud classification using Atrous XCRF Type de document : Article/Communication Auteurs : Hasan Asy’ari Arief, Auteur ; Ulf Geir Indahl, Auteur ; Geir-Harald Strand, Auteur ; Håvard Tveite, Auteur Année de publication : 2019 Article en page(s) : pp 90 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] champ aléatoire conditionnel [Termes IGN] classification automatique [Termes IGN] réseau neuronal convolutif [Termes IGN] réseau neuronal profond [Termes IGN] semis de points Résumé : (Auteur) Advances in techniques for automated classification of point cloud data introduce great opportunities for many new and existing applications. However, with a limited number of labelled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 85.0% in term of overall accuracy, and 71.1% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score. Additionally, transfer learning using the Bergen 2018 dataset, without model retraining, was also performed. Even though our proposal provides a consistent 3% improvement in term of accuracy, more work still needs to be done to alleviate the generalization problem on the domain adaptation and the transfer learning field. Numéro de notice : A2019-312 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.002 Date de publication en ligne : 11/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93337 [article]

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