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14 December 2020 Optical Remote Sensing Ship Image Classification Based on Deep Feature Combined Distance Metric Learning
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Zhao, S.; Xu, Y.; Li, W., and Lang, H., 2020. Optical remote sensing ship image classification based on deep feature combined distance metric learning. 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. 82-87. Coconut Creek (Florida), ISSN 0749-0208.

Image classification of ships has become an active research topic in the field of remote sensing. Effectively distinguishing specific category of a ship is crucial for many maritime matters. At present, there are two major difficulties in ship classification tasks. Firstly, the construction of traditional artificial features not only requires the assistance of professional knowledge, but also fails to represent the rich semantic information of ship images. Secondly, the classification of ships requires the identification of subcategory, the problem of intra-class diversity and inter-class similarity has become more serious and requires more careful handling. In order to solve the above problems, this paper proposes a method combining the deep feature and the distance metric learning (DML) algorithm to implement the classification of ships in the optical remote sensing images. In this study, an improved fine-tuned AlexNet convolution neural network (CNN) is adopted to extract the deep feature of ship images. The DML algorithm is applied to learn a good distance metric to reveal the similarity and dissimilarity between ships which are represented by deep feature. Comprehensive experiments show that the classification performance of deep feature combined DML algorithm significantly outperforms the comparative methods.

©Coastal Education and Research Foundation, Inc. 2020
Shuangmei Zhao, Yongjie Xu, Wenjing Li, and Haitao Lang "Optical Remote Sensing Ship Image Classification Based on Deep Feature Combined Distance Metric Learning," Journal of Coastal Research 102(sp1), 82-87, (14 December 2020).
Received: 29 June 2020; Accepted: 22 October 2020; Published: 14 December 2020

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