Matches in SemOpenAlex for { <https://semopenalex.org/work/W4292054920> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W4292054920 abstract "In recent years, concern has been increased on marine resources due to illegal fishing activities and the degradation of ecosystems. Earth's surface is roughly covered by 70% of water. Few countries use these water bodies as a buffer zone to safeguard their countries from threats. They also serve as a mode of transportation as well as shelter for a huge number of marine lives. In consequence, tracking various types of marine vessels and their activities demand to focus on new research and extracting the underlying information. The radar images captured by satellites are extremely useful in solving these problems. In this paper, various types of Artificial Neural Networks (ANNs) have been used to find out various patterns of marine vessels through SAR-captured images. Specifically, a few classification algorithms like CNN, ResNet50, and VGG-16 have been applied to classify maritime vessels. It is expected that using the classification model with the highest accuracy can be utilized to predict the vessel’s pattern which would be helpful for concerned authorities to monitor the marine traffic and restrict the vessels from illegal entry into water bodies. It is also concluded that CNN is the best model providing an accuracy of 99.25% by defeating the ResNet50 and VGG-16 models." @default.
- W4292054920 created "2022-08-17" @default.
- W4292054920 creator A5003736904 @default.
- W4292054920 creator A5005117364 @default.
- W4292054920 creator A5006798906 @default.
- W4292054920 creator A5057461565 @default.
- W4292054920 creator A5061986192 @default.
- W4292054920 date "2022-07-20" @default.
- W4292054920 modified "2023-10-16" @default.
- W4292054920 title "Classification of Marine Vessels using Deep Learning Models based on SAR Images" @default.
- W4292054920 cites W1970764305 @default.
- W4292054920 cites W2410591237 @default.
- W4292054920 cites W2544339223 @default.
- W4292054920 cites W2770553941 @default.
- W4292054920 cites W2774618766 @default.
- W4292054920 cites W2786644902 @default.
- W4292054920 cites W2885291434 @default.
- W4292054920 cites W2903903899 @default.
- W4292054920 cites W3012162230 @default.
- W4292054920 cites W3160028928 @default.
- W4292054920 cites W2785749360 @default.
- W4292054920 doi "https://doi.org/10.1109/icict54344.2022.9850767" @default.
- W4292054920 hasPublicationYear "2022" @default.
- W4292054920 type Work @default.
- W4292054920 citedByCount "0" @default.
- W4292054920 crossrefType "proceedings-article" @default.
- W4292054920 hasAuthorship W4292054920A5003736904 @default.
- W4292054920 hasAuthorship W4292054920A5005117364 @default.
- W4292054920 hasAuthorship W4292054920A5006798906 @default.
- W4292054920 hasAuthorship W4292054920A5057461565 @default.
- W4292054920 hasAuthorship W4292054920A5061986192 @default.
- W4292054920 hasConcept C108583219 @default.
- W4292054920 hasConcept C110872660 @default.
- W4292054920 hasConcept C115961682 @default.
- W4292054920 hasConcept C120665830 @default.
- W4292054920 hasConcept C121332964 @default.
- W4292054920 hasConcept C144133560 @default.
- W4292054920 hasConcept C151152651 @default.
- W4292054920 hasConcept C154945302 @default.
- W4292054920 hasConcept C155202549 @default.
- W4292054920 hasConcept C18903297 @default.
- W4292054920 hasConcept C192209626 @default.
- W4292054920 hasConcept C205649164 @default.
- W4292054920 hasConcept C2780771206 @default.
- W4292054920 hasConcept C41008148 @default.
- W4292054920 hasConcept C50644808 @default.
- W4292054920 hasConcept C62649853 @default.
- W4292054920 hasConcept C75294576 @default.
- W4292054920 hasConcept C81363708 @default.
- W4292054920 hasConcept C86803240 @default.
- W4292054920 hasConceptScore W4292054920C108583219 @default.
- W4292054920 hasConceptScore W4292054920C110872660 @default.
- W4292054920 hasConceptScore W4292054920C115961682 @default.
- W4292054920 hasConceptScore W4292054920C120665830 @default.
- W4292054920 hasConceptScore W4292054920C121332964 @default.
- W4292054920 hasConceptScore W4292054920C144133560 @default.
- W4292054920 hasConceptScore W4292054920C151152651 @default.
- W4292054920 hasConceptScore W4292054920C154945302 @default.
- W4292054920 hasConceptScore W4292054920C155202549 @default.
- W4292054920 hasConceptScore W4292054920C18903297 @default.
- W4292054920 hasConceptScore W4292054920C192209626 @default.
- W4292054920 hasConceptScore W4292054920C205649164 @default.
- W4292054920 hasConceptScore W4292054920C2780771206 @default.
- W4292054920 hasConceptScore W4292054920C41008148 @default.
- W4292054920 hasConceptScore W4292054920C50644808 @default.
- W4292054920 hasConceptScore W4292054920C62649853 @default.
- W4292054920 hasConceptScore W4292054920C75294576 @default.
- W4292054920 hasConceptScore W4292054920C81363708 @default.
- W4292054920 hasConceptScore W4292054920C86803240 @default.
- W4292054920 hasLocation W42920549201 @default.
- W4292054920 hasOpenAccess W4292054920 @default.
- W4292054920 hasPrimaryLocation W42920549201 @default.
- W4292054920 hasRelatedWork W2911497689 @default.
- W4292054920 hasRelatedWork W2952813363 @default.
- W4292054920 hasRelatedWork W3029198973 @default.
- W4292054920 hasRelatedWork W3133861977 @default.
- W4292054920 hasRelatedWork W3167935049 @default.
- W4292054920 hasRelatedWork W3193565141 @default.
- W4292054920 hasRelatedWork W4226493464 @default.
- W4292054920 hasRelatedWork W4312417841 @default.
- W4292054920 hasRelatedWork W4360783045 @default.
- W4292054920 hasRelatedWork W4378678253 @default.
- W4292054920 isParatext "false" @default.
- W4292054920 isRetracted "false" @default.
- W4292054920 workType "article" @default.