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Proceedings Paper

Ship detection and classification in high-resolution remote sensing imagery using shape-driven segmentation method
Author(s): Chao Tao; Yihua Tan; Huajie Cai; Jinwen Tian
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Paper Abstract

High-resolution remote sensing imagery provides an important data source for ship detection and classification. However, due to shadow effect, noise and low-contrast between objects and background existing in this kind of data, traditional segmentation approaches have much difficulty in separating ship targets from complex sea-surface background. In this paper, we propose a novel coarse-to-fine segmentation strategy for identifying ships in 1-meter resolution imagery. This approach starts from a coarse segmentation by selecting local intensity variance as detection feature to segment ship objects from background. After roughly obtaining the regions containing ship candidates, a shape-driven level-set segmentation is used to extract precise boundary of each object which is good for the following stages such as detection and classification. Experimental results show that the proposed approach outperforms other algorithms in terms of recognition accuracy.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74954N (30 October 2009); doi: 10.1117/12.830245
Show Author Affiliations
Chao Tao, Huazhong Univ. of Science and Technology (China)
Yihua Tan, Huazhong Univ. of Science and Technology (China)
Huajie Cai, Huazhong Univ. of Science and Technology (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Bruce Hirsch; Zhiguo Cao; Hanqing Lu, Editor(s)

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