A waterborne salient ship detection method on SAR imagery

Ma, Long; Chen, Liang; Zhang, Xue‐Jing; Chen, He; Soomro, Nouman Qadeer

doi:10.1007/s11432-015-5347-1

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Nevertheless, it is difficult to build and solve a proper statistical model for a complex sea area that is characterized by an uneven surface, inshore surface, inland river surface, etc. Therefore, these methods can cause false alarms because of obstructions, such as small islands, garbage, and floating objects [12,13].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2019

Remote Sensing

141

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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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Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2019

Remote Sensing

141

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“…With the rapid development of remote sensing spaceborne technologies, such as Sentinel-1, TerraSAR-X, and RADARSAT-2 [1][2][3], target detection on remote sensing images has been playing an important position in the field of civil areas and defense security [4][5][6]. However, target detection in remote sensing images remains a great challenge due to the complex background and existence of speckle noises in remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

CISPNet: Automatic Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Context Information Scene Perception

et al. 2019

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The ability to detect small targets and the speed of the target detector are very important for the application of remote sensing image detection, and in this paper, we propose an effective and efficient method (named CISPNet) with high detection accuracy and compact architecture. In particular, according to the characteristics of the data, we apply a context information scene perception (CISP) module to obtain the contextual information for targets of different scales and use k-means clustering to set the aspect ratios and size of the default boxes. The proposed method inherits the network structure of Single Shot MultiBox Detector (SSD) and introduces the CISP module into it. We create a dataset in the Pascal Visual Object Classes (VOC) format, annotated with the three types of detection targets, aircraft, ship, and oiltanker. Experimental results on our remote sensing image dataset as well as the Northwestern Polytechnical University very-high-resolution (NWPU VRH-10) dataset demonstrate that the proposed CISPNet performs much better than the original SSD and other detectors especially for small objects. Specifically, our network can achieve 80.34% mean average precision (mAP) at the speed of 50.7 frames per second (FPS) with the input size 300 × 300 pixels on the remote sensing image dataset. On extended experiments, the performance of CISPNet in fuzzy target detection in remote sensing image is better than that of SSD.

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“…However, SAR ship detection faces numerous challenges. First, ships encounter complex background conditions, such as in inshore, offshore, and inland river locations, and sometimes they are distracted by oil spills and small islands that are visually similar to the ship targets [4]. Even worse, ships are small in size and are usually densely clustered.…”

Section: Introductionmentioning

confidence: 99%

A coupled convolutional neural network for small and densely clustered ship detection in SAR images

Zhao

Guo

Zhang

et al. 2018

Sci. China Inf. Sci.

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Ship detection from synthetic aperture radar (SAR) imagery plays a significant role in global marine surveillance. However, a desirable performance is rarely achieved when detecting small and densely clustered ship targets, and this problem is difficult to solve. Recently, convolutional neural networks (CNNs) have shown strong detection power in computer vision and are flexible in complex background conditions, whereas traditional methods have limited ability. However, CNNs struggle to detect small targets and densely clustered ones that exist widely in many SAR images. To address this problem while preserving the good properties for complex background conditions, we develop a coupled CNN for small and densely clustered SAR ship detection. The proposed method mainly consists of two subnetworks: an exhaustive ship proposal network (ESPN) for ship-like region generation from multiple layers with multiple receptive fields, and an accurate ship discrimination network (ASDN) for false alarm elimination by referring to the context information of each proposal generated by ESPN. The motivation in ESPN is to generate as many ship proposals as possible, and in ASDN, the goal is to obtain the final results accurately. Experiments are evaluated on two data sets. One is collected from 60 wide-swath Sentinel-1 images and the other is from 20 GaoFen-3 (GF-3) images. Both data sets contain many ships that are small and densely clustered. The quantitative comparison results illustrate the clear improvements of the new method in terms of average precision (AP) and F 1 score by 0.4028 and 0.3045 for the Sentinel-1 data set compared with the multi-step constant false alarm rate (CFAR-MS) method. The values are verified as 0.2033 and 0.1522 for the GF-3 data set. In addition, the new method is demonstrated to be more efficient than CFAR-MS.

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A waterborne salient ship detection method on SAR imagery

Abstract: CitationMa L, Chen L, Zhang X J, et al. A waterborne salient ship detection method on SAR imagery. Sci China Inf Sci, 2015, 58: 089301(3),

Cited by 6 publications

References 10 publications

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

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

CISPNet: Automatic Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Context Information Scene Perception

A coupled convolutional neural network for small and densely clustered ship detection in SAR images

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