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Open AccessArticle

A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images

Department of Information Engineering, Harbin Engineering University, Harbin 150001, China
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(6), 276; https://doi.org/10.3390/ijgi8060276
Received: 26 February 2019 / Revised: 20 May 2019 / Accepted: 26 May 2019 / Published: 13 June 2019
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PDF [6329 KB, uploaded 13 June 2019]
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Abstract

In this paper, we propose a data augmentation method for ship detection. Inshore ship detection using optical remote sensing imaging is a challenging task owing to an insufficient number of training samples. Although the multilayered neural network method has achieved excellent results in recent research, a large number of training samples is indispensable to guarantee the accuracy and robustness of ship detection. The majority of researchers adopt such strategies as clipping, scaling, color transformation, and flipping to enhance the samples. Nevertheless, these methods do not essentially increase the quality of the dataset. A novel data augmentation strategy was thus proposed in this study by using simulated remote sensing ship images to augment the positive training samples. The simulated images are generated by true background images and three-dimensional models on the same scale as real ships. A faster region-based convolutional neural network (Faster R-CNN) based on Res101netwok was trained by the dataset, which is composed of both simulated and true images. A series of experiments is designed under small sample conditions; the experimental results show that better detection is obtained with our data augmentation strategy. View Full-Text
Keywords: data augmentation; optical remote sensing image; ship detection; simulated samples; deep learning data augmentation; optical remote sensing image; ship detection; simulated samples; deep learning
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Yan, Y.; Tan, Z.; Su, N. A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images. ISPRS Int. J. Geo-Inf. 2019, 8, 276.

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