Enhancement of Ship Type Classification from a Combination of CNN and KNN
SCIE
SCOPUS
Cited 11 time in
WEB OF SCIENCE
Cited 15 time in
Scopus
-
Title
- Enhancement of Ship Type Classification from a Combination of CNN and KNN
-
Author(s)
- Jeon, Ho-Kun; Yang, Chan-Su
- KIOST Author(s)
- Yang, Chan Su(양찬수)
-
Alternative Author(s)
- 전호군; 양찬수
-
Publication Year
- 2021-05
-
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.
-
ISSN
- 2079-9292
-
URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/41308
-
DOI
- 10.3390/electronics10101169
-
Bibliographic Citation
- ELECTRONICS, v.10, no.10, 2021
-
Publisher
- MDPI
-
Subject
- IDENTIFICATION; RECOGNITION; NETWORK
-
Keywords
- ship classification; CNN; KNN; Sentinel-1
-
Type
- Article
-
Language
- English
-
Document Type
- Article
- Files in This Item:
-
There are no files associated with this item.
Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.