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

Yiming Yan,Nan Su,Zhichao Tan

doi:10.3390/ijgi8060276

Yiming Yan, Nan Su + Show 1 more

Open Access

https://doi.org/10.3390/ijgi8060276

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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.

Highlights

Technology aimed at the detection of ships has prospects for wide application in the military and civilian fields
With the development of remote sensing technology, inshore ship detection has become a hot topic for the application of remote sensing images
Zou et al proposed singular value decompensation network (SVD-net), which is designed on the basis of the convolutional neural network and the singular value decompensation algorithm [2]

Summary

Introduction

Technology aimed at the detection of ships has prospects for wide application in the military and civilian fields. Yang et al proposed a framework called Rotation Dense Feature Pyramid Networks (R-DFPN), which effectively detects ships in different scenes, including oceans and ports [11] They proposed a rotation anchor strategy to predict the minimum circumscribed rectangle, reduce the size of the redundant detection region, and improve recall, achieving excellent results. These deep artificial neural networks require a large corpus of training data in order to work effectively. Photometric transformations amend color channels, offering changes in illumination and color [12] together with color jittering, noise perturbation, and Fancy Principle Component Analysis (PCA) In addition to these methods, a number of new approaches have been proposed and good results have been obtained with them. The application of this method to inshore images saves time and effort as compared to manual annotation

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Performance Evaluation

Method

Conclusions