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

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.

Arbitrary-Oriented Inshore Ship Detection based on Multi-Scale Feature Fusion and Contextual Pooling on Rotation Region Proposals

“…A fully convolutional network is applied to detect ships in the literature [35], but facing the problem that localization is not accurate enough. Methods based on R-CNN and its improved variants (especially Fast R-CNN and Faster R-CNN) are highly favored for their better detection effects [22]. Moreover, various improvements on the detection framework are employed, including the addition of ship head detection [23] and contextual information [1].…”

“…Since the networks mentioned above have been proven to be effective in object detection of natural images, researchers in remote sensing have made efforts to utilize them in the application of inshore ship detection. Zhang et al, realize ship detection based on a Faster R-CNN framework [22]. Wu et al, employ ship head searching, RPN, mutli-task network and NMS for inshore ship detection [23].…”

A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images

“…In order to quantitatively demonstrate the superiority of our approach, we compared it with the other object detection algorithms. We choose R-CNN [33], Faster R-CNN [28], SSD [37] and the latest ship detection algorithms [49] as the comparison algorithm. R-CNN is an object detection model based on deep convolutional neural network and has been widely used in object detection of remote sensing images.…”

Priority Branches for Ship Detection in Optical Remote Sensing Images

“…Although the method is effective, full overlap was needed between the existing classes across SAR and EO domains. Inspired by the Faster-RCNN detection framework [19], Zhang et al [20] proved their improved Faster-RCNN could achieve a higher recall and accuracy for small ships and gathering ships. However, ships abreast with each other still cannot be detected easily.…”