Share Email Print

Proceedings Paper • new

A public dataset for ship classification in remote sensing images
Author(s): Yanghua Di; Zhiguo Jiang; Haopeng Zhang; Gang Meng
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. It is well known that datasets have played an important role in object classification research, especially for CNN-based algorithms which have been proved to perform well. In this paper, we introduce a public Dataset for Ship Classification in Remote sensing images (DSCR). We collect 1,951 remote sensing images from DOTA, HRSC2016, NWPU VHR-10 and Google Earth, containing warships and civilian ships of various scales. For object classification, we cut out ships of different categories from the collected images. The whole dataset contains about 20,675 instances which are divided into seven categories, i.e. aircraft carrier, destroyer, assault ship, combat ship, cruiser, other military ship and civilian ship. Each image contains ships of the same category, which is labeled by the category name. Since our dataset contains most models of major warships, it is relatively comprehensive for ship classification. To build a benchmark for ship classification, we evaluated six popular CNN-based object classification algorithms on our dataset, including ResNet, ResNext, VGG, GoogLeNet, DenseNet, and AlexNet. Experiments demonstrates that our dataset can be used for verifying ship classification algorithms and may advance the development of ship classification in remote sensing images.

Paper Details

Date Published: 7 October 2019
PDF: 7 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111551M (7 October 2019); doi: 10.1117/12.2532741
Show Author Affiliations
Yanghua Di, Beihang Univ. (China)
Zhiguo Jiang, Beihang Univ. (China)
Haopeng Zhang, Beihang Univ. (China)
Key Lab. of Spacecraft Design Optimization and Dynamic Simulation Technologies (China)
Beijing Key Lab. of Digital Media (China)
Gang Meng, Beijing Institute of Remote Sensing Information (China)

Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

© SPIE. Terms of Use
Back to Top