SATELLITE IMAGE-BASED SHIP CLASSIFICATION METHOD WITH SENTINEL-1 IW MODE DATA

Title
SATELLITE IMAGE-BASED SHIP CLASSIFICATION METHOD WITH SENTINEL-1 IW MODE DATA
Author(s)
김승룡; 배정주; 양찬수
KIOST Author(s)
Kim, Seungryong(김승룡)Yang, Chan Su(양찬수)
Publication Year
2019-07-29
Abstract
Classification of a ship based on satellite imagery usually results differently depending on the type of polarization used in image generation. Also, the different ship’s orientation in each image degrades the performance of image-based classification. Given these points, Sentinel-1 data also needs some methods to classify the type of ship. For the classification, we have produced a ship dataset, KIOST-OpenSARShip, which was modified from the OpenSARShip dataset. We compared the brightness of each pixel of ship images generated by different polarizations. Based on this, we created a new image dataset. Then, we increased the similarity between ship images of the same type by aligning the heading direction in the ship images. As a result, our new datasets improve classification performances in some cases compared to using the OpenSARShip. The results of composite images from the VV- and VH-polarized images show up to 19.34% higher accuracy than those using only the one polarized images. In the future, we will improve the performance of the ship classification method considering various characteristics of the ship.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/22544
Bibliographic Citation
2019 IEEE International Geoscience and Remote Sensing Symposium, pp.1300 - 1301, 2019
Publisher
IEEE Geoscience and Remote Sensing Society
Type
Conference
Language
English
Publisher
IEEE Geoscience and Remote Sensing Society
Related Researcher
Research Interests

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

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