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Performance evaluation of sea surface simulation methods for target detection
Author(s): Renjie Xia; Xin Wu; Chen Yang; Yiping Han; Jianqi Zhang
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Paper Abstract

With the fast development of sea surface target detection by optoelectronic sensors, machine learning has been adopted to improve the detection performance. Many features can be learned from training images by machines automatically. However, field images of sea surface target are not sufficient as training data. 3D scene simulation is a promising method to address this problem. For ocean scene simulation, sea surface height field generation is the key point to achieve high fidelity. In this paper, two spectra-based height field generation methods are evaluated. Comparison between the linear superposition and linear filter method is made quantitatively with a statistical model. 3D ocean scene simulating results show the different features between the methods, which can give reference for synthesizing sea surface target images with different ocean conditions.

Paper Details

Date Published: 15 November 2017
PDF: 19 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060516 (15 November 2017); doi: 10.1117/12.2286830
Show Author Affiliations
Renjie Xia, Xidian Univ. (China)
Xin Wu, Xidian Univ. (China)
Chen Yang, Xi'an Univ. of Technology (China)
Yiping Han, Xidian Univ. (China)
Jianqi Zhang, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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