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Remote Sens. 2019, 11(6), 631; https://doi.org/10.3390/rs11060631

R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery

1
College of Surveying, Mapping and Geo-Informatics, Tongji University, Shanghai 200092, China
2
Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, Zhejiang, China
*
Author to whom correspondence should be addressed.
Received: 26 January 2019 / Revised: 5 March 2019 / Accepted: 11 March 2019 / Published: 15 March 2019
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Abstract

Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery. View Full-Text
Keywords: ship detection; regional convolutional neural network; GaoFen-2 remote sensing image; small ship; gathering ship ship detection; regional convolutional neural network; GaoFen-2 remote sensing image; small ship; gathering ship
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, S.; Wu, R.; Xu, K.; Wang, J.; Sun, W. R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery. Remote Sens. 2019, 11, 631.

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