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INVESTIGATING FULLY CONVOLUTIONAL NETWORK TO SEMANTIC LABELLING OF BATHYMETRIC POINT CLOUD

التفاصيل البيبلوغرافية
العنوان: INVESTIGATING FULLY CONVOLUTIONAL NETWORK TO SEMANTIC LABELLING OF BATHYMETRIC POINT CLOUD
المؤلفون: S. Daniel, V. Dupont
المصدر: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 657-663 (2020)
بيانات النشر: Copernicus Publications, 2020.
سنة النشر: 2020
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Applied optics. Photonics
مصطلحات موضوعية: Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
الوصف: The benefit of autonomous vehicles in hydrography is largely based on the ability of these platforms to carry out survey campaigns in a fully autonomous manner. One solution is to have real-time processing onboard the survey vessel. To meet this real-time processing goal, deep learning based-models are favored. Although Artificial Intelligence (AI) is booming, the main studies have been devoted to optical images and more recently, to LIDAR point clouds. However, little attention has been paid to the underwater environment. In this paper, we present an investigation into the adaptation of deep neural network to multi-beam echo-sounder (MBES) point cloud in order to classify sea-bottom morphology. More precisely, the paper investigates whether fully convolutional network can be trained while using the native 3D structure of the point cloud. A preprocessing approach is provided in order to overcome the lack of adequate training data. The results reported from the test data sets show the level of complexity related to natural, underwater terrain features where a classification accuracy no better than 65% can be reached when 2 micro topographic classes are used. Point density and resolution have a strong impact on the seabed morphology thereby affecting the classification scheme.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2194-9042
2194-9050
Relation: https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/657/2020/isprs-annals-V-2-2020-657-2020.pdf; https://doaj.org/toc/2194-9042; https://doaj.org/toc/2194-9050
DOI: 10.5194/isprs-annals-V-2-2020-657-2020
URL الوصول: https://doaj.org/article/826805b274684e3e92c0e2e879474e60
رقم الأكسشن: edsdoj.826805b274684e3e92c0e2e879474e60
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21949042
21949050
DOI:10.5194/isprs-annals-V-2-2020-657-2020