Ship detection and classification in high-resolution remote sensing imagery using shape-driven segmentation method

Chao Tao; Yihua Tan; Huajie Cai; Jinwen Tian

doi:10.1117/12.830245

30 October 2009 Ship detection and classification in high-resolution remote sensing imagery using shape-driven segmentation method

Chao Tao, Yihua Tan, Huajie Cai, Jinwen Tian

Author Affiliations +

Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74954N (2009) https://doi.org/10.1117/12.830245
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Chao Tao, Yihua Tan, Huajie Cai, and Jinwen Tian "Ship detection and classification in high-resolution remote sensing imagery using shape-driven segmentation method", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74954N (30 October 2009); https://doi.org/10.1117/12.830245

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