Enhancement of Ship Type Classification from a Combination of CNN and KNN SCIE SCOPUS
DC Field | Value | Language |
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dc.contributor.author | Jeon, Ho-Kun | - |
dc.contributor.author | Yang, Chan-Su | - |
dc.date.accessioned | 2021-05-19T23:50:01Z | - |
dc.date.available | 2021-05-19T23:50:01Z | - |
dc.date.created | 2021-05-18 | - |
dc.date.issued | 2021-05 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/41308 | - |
dc.description.abstract | Ship type classification of synthetic aperture radar imagery with convolution neural network (CNN) has been faced with insufficient labeled datasets, unoptimized and noised polarization images that can deteriorate a classification performance. Meanwhile, numerous labeled text information for ships, such as length and breadth, can be easily obtained from various sources and can be utilized in a classification with k-nearest neighbor (KNN). This study proposes a method to improve the efficiency of ship type classification from Sentinel-1 dual-polarization data with 10 m pixel spacing using both CNN and KNN models. In the first stage, Sentinel-1 intensity images centered on ship positions were used in a rectangular shape to apply an image processing procedure such as head-up, padding and image augmentation. The process increased the accuracy by 33.0% and 31.7% for VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization compared to the CNN-based classification with original ship images, respectively. In the second step, a combined method of CNN and KNN was compared with a CNN-alone case. The f1-score of CNN alone was up to 85.0%, whereas the combination method showed up to 94.3%, which was a 9.3% increase. In the future, more details on an optimization method will be investigated through field experiments of ship classification. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.subject | IDENTIFICATION | - |
dc.subject | RECOGNITION | - |
dc.subject | NETWORK | - |
dc.title | Enhancement of Ship Type Classification from a Combination of CNN and KNN | - |
dc.type | Article | - |
dc.citation.title | ELECTRONICS | - |
dc.citation.volume | 10 | - |
dc.citation.number | 10 | - |
dc.contributor.alternativeName | 전호군 | - |
dc.contributor.alternativeName | 양찬수 | - |
dc.identifier.bibliographicCitation | ELECTRONICS, v.10, no.10 | - |
dc.identifier.doi | 10.3390/electronics10101169 | - |
dc.identifier.scopusid | 2-s2.0-85105929386 | - |
dc.identifier.wosid | 000655108200001 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | NETWORK | - |
dc.subject.keywordAuthor | ship classification | - |
dc.subject.keywordAuthor | CNN | - |
dc.subject.keywordAuthor | KNN | - |
dc.subject.keywordAuthor | Sentinel-1 | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Physics | - |