A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection

Jiao, Jiao; Zhang, Yue; Sun, Hao; Yang, Xue; Gao, Xun; Hong, Wen; Fu, Kun; Sun, Xian

doi:10.1109/access.2018.2825376

Cited by 237 publications

(130 citation statements)

References 25 publications

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“…SAR images in this dataset possess different satellite sensors, various polarization modes, multiple resolutions, different scenes, and abundant ship sizes, so it can verify the robustness of methods. Therefore, many scholars [10,21,35,[55][56][57][58][59][60][61][62][63] conducted research based on it for a better comparison.…”

Section: Datasetmentioning

confidence: 99%

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

et al. 2019

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As an active microwave imaging sensor for the high-resolution earth observation, synthetic aperture radar (SAR) has been extensively applied in military, agriculture, geology, ecology, oceanography, etc., due to its prominent advantages of all-weather and all-time working capacity. Especially, in the marine field, SAR can provide numerous high-quality services for fishery management, traffic control, sea-ice monitoring, marine environmental protection, etc. Among them, ship detection in SAR images has attracted more and more attention on account of the urgent requirements of maritime rescue and military strategy formulation. Nowadays, most researches are focusing on improving the ship detection accuracy, while the detection speed is frequently neglected, regardless of traditional feature extraction methods or modern deep learning (DL) methods. However, the high-speed SAR ship detection is of great practical value, because it can provide real-time maritime disaster rescue and emergency military planning. Therefore, in order to address this problem, we proposed a novel high-speed SAR ship detection approach by mainly using depthwise separable convolution neural network (DS-CNN). In this approach, we integrated multi-scale detection mechanism, concatenation mechanism and anchor box mechanism to establish a brand-new light-weight network architecture for the high-speed SAR ship detection. We used DS-CNN, which consists of a depthwise convolution (D-Conv2D) and a pointwise convolution (P-Conv2D), to substitute for the conventional convolution neural network (C-CNN). In this way, the number of network parameters gets obviously decreased, and the ship detection speed gets dramatically improved. We experimented on an open SAR ship detection dataset (SSDD) to validate the correctness and feasibility of the proposed method. To verify the strong migration capacity of our method, we also carried out actual ship detection on a wide-region large-size Sentinel-1 SAR image. Ultimately, under the same hardware platform with NVIDIA RTX2080Ti GPU, the experimental results indicated that the ship detection speed of our proposed method is faster than other methods, meanwhile the detection accuracy is only lightly sacrificed compared with the state-of-art object detectors. Our method has great application value in real-time maritime disaster rescue and emergency military planning.

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Section: Datasetmentioning

confidence: 99%

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

et al. 2019

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“…Similarly, kernel numbers and sizes during the fine-detection stage were confirmed using network comparisons shown in Table 2c,d. Detection performance was evaluated using four typical measures, including figure of merit (FoM), precision, recall, and F-measure [19,43], respectively. They are defined as follows:…”

Section: Discussion Of Parameter Configuration Of H-cnnmentioning

confidence: 99%

“…Some other well-known CNN-based target detection methods include faster region-CNN (Faster R-CNN), you only look once (YOLO) list model, etc. For example, Li et al [18,19] improved detection performance using Faster R-CNN, to successfully provide a densely connected multi-scale neural network [19]. This method is used to solve multi-scale and multi-scene problems in SAR ship detection.…”

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confidence: 99%

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

et al. 2019

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The ghost phenomenon in synthetic aperture radar (SAR) imaging is primarily caused by azimuth or range ambiguities, which cause difficulties in SAR target detection application. To mitigate this influence, we propose a ship target detection method in spaceborne SAR imagery, using a hierarchical convolutional neural network (H-CNN). Based on the nature of ghost replicas and typical target classes, a two-stage CNN model is built to detect ship targets against sea clutter and the ghost. First, regions of interest (ROIs) were extracted from a large imaged scene during the coarse-detection stage. Unwanted ghost replicas represented major residual interference sources in ROIs, therefore, the other CNN process was executed during the fine-detection stage. Finally, comparative experiments and analyses, using Sentinel-1 SAR data and various assessment criteria, were conducted to validate H-CNN. Our results showed that the proposed method can outperform the conventional constant false-alarm rate technique and CNN-based models.Remote Sens. 2019, 11, 620 2 of 17 recognition [11]. In addition, the conventional constant false alarm rate (CFAR) technique is a typical detection method based on the grayscale feature. However, complicated and cluttered backgrounds severely affect CFAR detection performance [12].In recent years, ship target detection based on deep learning (DL) has been widely studied [13,14], using the typical model of convolutional neural network (CNN) [15]. Liu et al. [16] presented a ship detection method, namely sea-land segmentation-based convolutional neural network (SLS-CNN), which combines a SLS-CNN detector, saliency computation, and corner features. Furthermore, Zhao et al. [17] proposed a spaceborne SAR ship detection algorithm based on low complexity CNN. Some other well-known CNN-based target detection methods include faster region-CNN (Faster R-CNN), you only look once (YOLO) list model, etc. For example, Li et al. [18,19] improved detection performance using Faster R-CNN, to successfully provide a densely connected multi-scale neural network [19]. This method is used to solve multi-scale and multi-scene problems in SAR ship detection. Feature maps are fused by densely connecting different feature map layers, rather than information from single feature maps, which represent top-to-down feature map connections. The R-CNN method is used for target recognition in large scene SAR images [20]. Furthermore, Hamza and Cai used YOLOv2 for ship detection [21], which introduced a multitude of enhancements into the original YOLO model.However, these methods may be no longer effective when ghost replicas exist in an imaged scene. The ghost phenomenon is an intrinsic effect of SAR's ambiguity, both in azimuth and range [22,23]. Range ambiguity occurs when different backscattered echoes-one related to a transmitted pulse and the other due to a previous transmission-temporarily overlap during the receiving operation [24]. On the other hand, azimuth ambiguity is caused by the aliasing of each target's Doppler phase...

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“…To solve this problem, in this work we propose a novel CNN structure to classify eight types of marine targets in SAR images with higher accuracy than the existing methods. As for the detection task, many algorithms including Constant False Alarm Rate (CFAR) based methods [21,22], feature based methods [23] and CNN based methods [24,25] have been developed to detect marine targets in SAR images. Among them, the CFAR based methods are the most widely used ones due to their simplicity.…”

mentioning

confidence: 99%

“…An end-to-end model called SSD was proposed in Reference [31], which can detect the target at real time with high accuracy. It generates region proposals on several feature maps of different scales while Faster-RCNN proposes region candidates with different sizes on the last feature map provided by the deep convolutional network.The CNN based methods have been used for target detection in SAR images, e.g., ship detection [24] and land target detection [33], and has shown a better performance than the traditional methods. One method splits the images into small patches and then uses the pre-trained CNN model to classify the patches, after which the classification results are mapped onto the original images [34].…”

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confidence: 99%

Ship Classification and Detection Based on CNN Using GF-3 SAR Images

Chen

Liu

et al. 2018

Remote Sensing

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Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection.

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A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection

Cited by 237 publications

References 25 publications

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

Ship Classification and Detection Based on CNN Using GF-3 SAR Images

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