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MULTI-MODAL DEEP LEARNING WITH SENTINEL-3 OBSERVATIONS FOR THE DETECTION OF OCEANIC INTERNAL WAVES

التفاصيل البيبلوغرافية
العنوان: MULTI-MODAL DEEP LEARNING WITH SENTINEL-3 OBSERVATIONS FOR THE DETECTION OF OCEANIC INTERNAL WAVES
المؤلفون: L. Drees, J. Kusche, R. Roscher
المصدر: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 813-820 (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 observation of waves that propagate along density interfaces inside the ocean poses a significant challenge, as their visible surface signatures are much lower compared to their internal amplitudes. However, monitoring internal waves is important as they redistribute large amounts of energy, play a role in mixing and vertical heat transfer, and modify water and nutrient transports. Although satellite observations would allow global monitoring of internal waves at constant time intervals, their automatic detection is challenging: In optical images, internal waves are hardly visible and can be obscured by clouds, whereas radar data have limitations in coastal regions and their spatial coverage is not perfect. Furthermore, the occurrence of internal waves can be confused with other ocean phenomena. In this work, we present an automated detection framework for internal waves based on multiple data sources in order to compensate for the shortcoming of single data sources. In our application, we use Ocean and Land Color Imager and Synthetic Aperture Radar Altimeter data. Our contributions are (1) we develop a multi-modal deep neural network SONet with multi-streams and late fusion, which performs a classification on the basis of training with both modalities, and (2) we establish a method to deal with missing modalities. Experiments in the Amazon Shelf region show SONet achieves adequate results when both modalities are available, but also when only a single modality is available. By exploiting correlations between the modalities, SONet classifies OLCI images off the SRAL ground track better than uni-modal network ONet, which describes a great advantage of our multi-modal network.
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-2-2020/813/2020/isprs-annals-V-2-2020-813-2020.pdf; https://doaj.org/toc/2194-9042; https://doaj.org/toc/2194-9050
DOI: 10.5194/isprs-annals-V-2-2020-813-2020
URL الوصول: https://doaj.org/article/5cb2f48eeb3a4f75a06a63d6f88d6517
رقم الأكسشن: edsdoj.5cb2f48eeb3a4f75a06a63d6f88d6517
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21949042
21949050
DOI:10.5194/isprs-annals-V-2-2020-813-2020