Enhancement of Ship Type Classification from a Combination of CNN and KNN SCIE SCOPUS

Cited 1 time in WEB OF SCIENCE Cited 1 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)
Jeon, Ho Kun(전호군)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
Publisher
MDPI
Related Researcher
Research Interests

Satellite Oceanography,Marine Safety & Security,Remote Sensing,위성해양학,해양 안전 및 보안,원격탐사

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