Ship Matching Using Convolutional Neural Network in Multi-source Synthetic Aperture Radar Images

• Published in: Journal of coastal research Vol. 102; no. sp1; pp. 166 - 175
• Main Authors: Niu, Lihui, Lang, Haitao
• Format: Journal Article
• Language: English
• Published: Fort Lauderdale Coastal Education and Research Foundation 01.09.2020; Allen Press Inc
• Subjects: Artificial neural networks; Coastal environments; Coastal inlets; Coastal research; Coastal zones; Computer architecture; constrained hinge loss; Convolution; Deformation; Direction; Distance; Horizontal orientation; Image quality; Image segmentation; Incidence angle; Invariance; Matching; Methods; Neural networks; Performance enhancement; Preprocessing; Radar; Radar imaging; Rotation; SAR (radar); Satellite imagery; Satellite tracking; ship tracking; Ships; Similarity; Synthetic aperture radar; Two-channel network
• ISSN: 0749-0208; 1551-5036
• DOI: 10.2112/SI102-021.1

Niu, L. and Lang, H., 2020. Ship matching using convolutional neural network in multi-source synthetic aperture radar images. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 166-175. Coconut Creek (Florida), ISSN 0749-0208. There are two major challenges in the ship matching and tracking task by synthetic aperture radar (SAR) images. First, images from different satellites have different incidence angles, polarization modes and resolutions, leading to the nonlinear geometric deformation of ships and the difference in image quality. The above differences result in a significant drop in the similarity of matching ships. Second, the principal direction of ships is arbitrary, which requires that the matching method has rotation-invariance. For the first challenge, the siamese-type convolution neural networks are adopted to reduce the distance between matching ships and increase the distance between non-matching ships. For the second challenge, a minimum bounding square (MBS) segmentation method is used to process all the ship images to the horizontal direction. After image preprocessing using MBS, there are only two situations for the principal direction of ships and rotation-invariance is transformed into the invariance of prow-to-stern interchange. To solve the problem of prow-to-stern interchange, a constrained hinge loss is proposed to ensure that the minimum similarity of matching images is still greater than the maximum similarity of non-matching images. There are a variety of siamese-type convolutional neural networks with good performance currently, the focus of the research is to use the two proposed strategies (MBS and constrained hinge loss) to improve the performance of existing methods instead of designing network architectures. In order to verify the feasibility of the proposed strategies, a multi-source ship target database covers different SAR sensors is constructed for ship matching. The experimental results prove that the proposed methods can match the ship target precisely and outperform the methods without MBS preprocessing and the constrained hinge loss function.