Broad Area Target Search System for Ship Detection via Deep Convolutional Neural Network

High-resolution optical remote sensing data can be utilized to investigate the human behavior and the activities of artificial targets, for example ship detection on the sea. Recently, the deep convolutional neural network (DCNN) in the field of deep learning is widely used in image processing, especially in target detection tasks. Therefore, a complete processing system called the broad area target search (BATS) is proposed based on DCNN in this paper, which contains data import, processing and storage steps. In this system, aiming at the problem of onshore false alarms, a method named as Mask-Faster R-CNN is proposed to differentiate the target and non-target areas by introducing a semantic segmentation sub network into the Faster R-CNN. In addition, we propose a DCNN framework named as Saliency-Faster R-CNN to deal with the problem of multi-scale ships detection, which solves the problem of missing detection caused by the inconsistency between large-scale targets and training samples. Based on these DCNN-based methods, the BATS system is tested to verify that our system can integrate different ship detection methods to effectively solve the problems that existed in the ship detection task. Furthermore, our system provides an interface for users, as a data-driven learning, to optimize the DCNN-based methods.

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

Remote Sensing ◽

10.3390/rs11060631 ◽

2019 ◽

Vol 11 (6) ◽

pp. 631 ◽

Cited By ~ 28

Author(s):

Shaoming Zhang ◽

Ruize Wu ◽

Kunyuan Xu ◽

Jianmei Wang ◽

Weiwei Sun

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Convolutional Neural Network ◽

Target Detection ◽

Detection Methods ◽

Optical Remote Sensing ◽

Remote Sensing Imagery ◽

Ship Detection ◽

Inland River

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.

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