Zobrazeno 1 - 10
of 225
pro vyhledávání: '"sediment classification"'
Autor:
Michael Anokye, Xiaodong Cui, Fanlin Yang, Ping Wang, Yuewen Sun, Hadong Ma, Emmanuel Oduro Amoako
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTSeabed sediment mapping with acoustical data and ground-truth samples is a growing field in marine science. In recent years, multi-classifier ensemble models have gained prominence for classification problems by combining several base classif
Externí odkaz:
https://doaj.org/article/b92ef7ec6c634a47b106c03d4e423e74
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 4, p 632 (2024)
Sub-bottom profile data have the potential to characterize sediment properties but are seldom used for offshore site investigations because of uncertainties in rock-physics models. Deep-learning techniques appear to be poised to play very important r
Externí odkaz:
https://doaj.org/article/5637f445f4814c2e99ce53bda0e076a6
Publikováno v:
Journal of Marine Science and Engineering, Vol 11, Iss 9, p 1770 (2023)
It has been proven that the quality factor (Q) is important for marine sediment attenuation attribute representation and is helpful for sediment classification. However, the traditional spectral-ratio (SR) method is affected by the interference effec
Externí odkaz:
https://doaj.org/article/363fcbc1f7c949a98a2061121db58b03
Publikováno v:
Journal of Geospatial Information Science and Engineering, Vol 4, Iss 2, Pp 140-148 (2021)
Pelabuhan Tanjung Perak Surabaya merupakan salah satu pelabuhan utama di Indonesia yang memiliki peran penting dalam transportasi laut Indonesia. Survei batimetri rutin diperlukan untuk mengidentifikasi kedalaman alur akses pelabuhan dan kondisi sedi
Externí odkaz:
https://doaj.org/article/6ce84bf57f084985b96c146fed5fb156
Publikováno v:
Remote Sensing, Vol 15, Iss 14, p 3576 (2023)
The classification of marine sediment based on acoustic data is crucial for various applications such as marine resource exploitation, marine engineering construction, and marine ecological environment maintenance. It serves as a valuable alternative
Externí odkaz:
https://doaj.org/article/24ae6ae822444981b20bb3a935f41846
Publikováno v:
Journal of Marine Science and Engineering, Vol 11, Iss 5, p 1074 (2023)
This paper proposed an MSC-Transformer model based on the Transformer’s neural network, which was applied to seabed sediment classification. The data came from about 2900 km2 of seabed area on the northern slope of the South China Sea. Using the su
Externí odkaz:
https://doaj.org/article/d42622554e6340ecaa1f7946cd2c0158
Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 2178 (2023)
Seabed sediment classification is of great significance in acoustic remote sensing. To accurately classify seabed sediments, big data are needed to train the classifier. However, acquiring seabed sediment information is expensive and time-consuming,
Externí odkaz:
https://doaj.org/article/f16ec9e789244f0b8088ff0a5e796cef
Publikováno v:
IEEE Access, Vol 9, Pp 53379-53391 (2021)
The exploitation and utilization of seabed sediments provide vital significance in many fields. Recently, the classification of seabed sediments using sub-bottom profiler(SBP) data has become a research focus. Specifically, SBP data can be applied no
Externí odkaz:
https://doaj.org/article/e6397d1cd5c14701a30c874d510baa1e
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Remote Sensing, Vol 14, Iss 15, p 3708 (2022)
High-precision habitat mapping can contribute to the identification and quantification of the human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment classification is crucial for marine geological research, marine
Externí odkaz:
https://doaj.org/article/ecc7726fca8245fc96ae1cd3bc37db96