Results 191 to 200 of about 27,061 (245)
Hybrid Underwater Image Enhancement via Dual Transmission Optimization and Transformer-Based Feature Fusion. [PDF]
Sensors (Basel)Hu N, Li S, Tan J.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Sound classification during Seabed Classification Experiment 2017
The Journal of the Acoustical Society of America, 2021In ocean acoustics, finding acoustic signals within long recordings is usually time consuming. The need to optimize this process we propose an experiment to discover the optimal acoustic signal classification model using the PyTorch deep learning package.
Allison N. Earnhardt +4 more
openaire +1 more source
IEEE Journal of Oceanic Engineering, 1988
The degree to which different seabed types may be discriminated using features of the power spectrum of the signals backscattered from the seabed, in a side-scan mode, is evaluated. The statistics derived from the data samples considered suggest that the probability of correctly classifying the six seabed types (sand, mud, clay, gravel, stones, rock ...
N.G. Pace, H. Gao
openaire +1 more source
The degree to which different seabed types may be discriminated using features of the power spectrum of the signals backscattered from the seabed, in a side-scan mode, is evaluated. The statistics derived from the data samples considered suggest that the probability of correctly classifying the six seabed types (sand, mud, clay, gravel, stones, rock ...
N.G. Pace, H. Gao
openaire +1 more source
Seabed classification using multibeam echosounder
2008 1st International Conference on Information Technology, 2008The method of seabed identification and classification from multibeam sonar echoes is presented. The proposed approach is based on calculation of a set of parameters of an echo envelope, similarly as in seafloor classification using single beam echosounder.
Zbigniew Lubniewski, Andrzej Chybicki
openaire +1 more source
Multisensor fusion for seabed classification
Proceedings of OCEANS 2005 MTS/IEEE, 2005Automatic seabed classification can be achieved using acoustic sensors but methods need to be improved. In order to get better classification reliability, we propose to use complementarity between several acoustic sensors: normal incidence echo sounder, sidescan sonar. The new feature is that the sonar (Klein), provides a high resolution sidescan sonar
D. Kerneis, B. Zerr
openaire +1 more source
Multibeam Backscatter Seabed Classification
4th Congress of the Balkan Geophysical Society, 2005GLOBAL CHANGES & MARINE GEOSCIENCES O12 - 05 MULTIBEAM BACKSCATTER SEABED CLASSIFICATION X. Monteys D. Inamdar Geologic Survey of Ireland Dublin Ireland The Irish National Seabed Survey is now into its sixth and final year of acquisition programme.
X. Monteys, D. Inamdar
openaire +1 more source
Multibeam seabed topographic classification
Oceans '97. MTS/IEEE Conference Proceedings, 2002The Naval Oceanographic Office (NAVOCEANO) conducts military oceanographic surveys that provide environmental products to the U.S. Navy. To meet these requirements, all NAVOCEANO survey ships have transitioned from single-beam echosounders to both shallow- and deep-water multibeam sonar systems.
J.A. Bunce, M.O. Clough
openaire +1 more source
Seabed Sub-Bottom Sediment Classification Using Artificial Intelligence
Journal of Coastal Research, 2021Kim, H.D.; Aoki, S.; Oh, H.; Kim, K.H.; and Oh, J., 2021. Seabed sub-bottom sediment classification using artificial intelligence. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 305–309.
Hyun Dong Kim +4 more
openaire +1 more source
Seabed classification of multibeam sonar images
MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295), 2002Seabed images, from multibeam hydrographic systems or from single or multibeam sidescans, convey a lot of information about seabed type. Statistical processing of portions of images can generate features adequate for seabed classification that agree with both large-scale interpretation and fine details.
J.M. Preston +3 more
openaire +1 more source
Environmental effects on seabed object bistatic scattering classification
The Journal of the Acoustical Society of America, 2017One of the factors that significantly affects bistatic scattering from seabed targets is bottom type. This factor has the potential to impact classification, as models that do not take bottom composition into account could improperly characterize target type, geometry, or material. This paper looks at the impact of bottom composition and self-burial on
Erin M, Fischell, Henrik, Schmidt
openaire +2 more sources