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ship segmentation via Encoder-Decoder Network With Global Attention in High-Resolution SAR Images

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Li, Jichao Gou, Shuiping Li, Ruimin Chen, Jia-Wei Sun, Xiaolong Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Shaanxi Peoples R China China Elect Technol Grp Corp 20th Res Inst Xian 710068 Shaanxi Peoples R China

ship detection in the synthetic aperture radar (SAR) image is of great significance in the fields of military and coastal defense. Most ship detection methods are designed based on the object detection framework, which can only provide the vertices' coordinates of the bounding box covering the ship targets but cannot provide more detailed contour information. Target segmentation can further explore the shape and edge information of the objects, which can be used as a blazing novel means for automatic object detection. In this letter, a 3-D atrous encoder-decoder neural network with global attention modules (GAM-EDNet) is proposed to achieve ship segmentation in SAR images. The encoder-decoder structure with atrous convolution is developed as the network body to fully exploit the structural information of the ship targets with various sizes. To increase the structural information of the single-polarization SAR images, a 3-D image cube is designed as the input of the GAM-EDNet. A global attention module is proposed to further improve the segmentation performance by integrating the high-level semantic features with the low-level location features. Besides, an SAR ship segmentation dataset (SAR-HR4) is built to evaluate the segmentation performance, and the experimental results show that the proposed GAM-EDNet achieves better performance than other state-of-the-art methods.

关键词： Marine vehicles Image segmentation Radar polarimetry Semantics Synthetic aperture radar Feature extraction Wavelet transforms Encoder-decoder structure global attention module high-resolution synthetic aperture radar (SAR) images ship segmentation

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REMOTE SENSING 2023年第13期15卷 3304-3304页

作者： Li, Ruimin Li, Jichao Gou, Shuiping Lu, Haofan Mao, Shasha Guo, Zhang Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Peoples R China Xidian Univ Key Lab Intelligent Percept Image Understanding Minist Educ Xian 710071 Peoples R China

Target detection and segmentation in synthetic aperture radar (SAR) images are vital steps for many remote sensing applications. In the era of data-driven deep learning, this task is extremely challenging due to the limited labeled data. Few-shot learning has the ability to learn quickly from a few samples with supervised information. Inspired by this, a few-shot learning framework named MSG-FN is proposed to solve the segmentation of ship targets in heterologous SAR images with few annotated samples. The proposed MSG-FN adopts a dual-branch network consisting of a support branch and a query branch. The support branch is used to extract features with an encoder, and the query branch uses a U-shaped encoder-decoder structure to segment the target in the query image. The encoder of each branch is composed of well-designed residual blocks combined with filter response normalization to capture robust and domain-independent features. A multi-scale similarity guidance module is proposed to improve the scale adaptability of detection by applying hand-on-hand guidance of support features to query features of various scales. In addition, a SAR dataset named SARship-4i is built to evaluate the proposed MSG-FN, and the experimental results show that the proposed method achieves superior segmentation results compared with the state-of-the-art.

关键词： SAR image ship segmentation few-shot learning multi-scale similarity guidance

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Near-Shore ship segmentation based on I-Polar Mask in remote sensing

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2022年第9期43卷 3470-3489页

作者： Zhang, Dongdong Wang, Chunping Fu, Qiang People Liberat Army Engn Univ Shijiazhuang Dept Elect & Opt Engn Shijiazhuang Hebei Peoples R China

With the development of deep convolutional neural network, the performance of instance segmentation algorithms has been significantly improved. However, it is difficult to implement quick and accurate near-shore ship segmentation due to the complex background and arbitrary ship orientation, which makes segmentation challenging. To improve the segmentation efficiency of near-shore ships, this paper proposes a ship segmentation network based on Polar Mask, named I-Polar Mask. Specifically, we construct a Specific Polar Template (SPT) and Oriented Polar IoU (Intersection over Union) to better match the ship contour. Furthermore, oriented polar centre-ness was designed to reduce the weight of low-quality masks more effectively. In addition, a Context Information Extraction Module (CIEM) is built to reduce the influence of complex backgrounds and make the segmentation more accurate. To verify the effectiveness of the proposed algorithm, we collected 1015 images of near-shore areas from Google Maps and labelled them with Labelme to construct a ship instance segmentation dataset, called I-ship. Extensive experiments on I-ship show that the AP value of I-Polar Mask is improved by 5.9% compared with Polar Mask, which is a significant improvement. Compared with advanced methods, I-Polar Mask outperforms both quantitative and qualitative aspects.

关键词： convolutional neural network remote sensing image instance segmentation ship segmentation

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An Efficient Cascaded Model for ship segmentation in Aerial Images

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IEEE ACCESS 2022年 10卷 31942-31954页

作者： Pires, Carlos Damas, Bruno Bernardino, Alexandre Inst Syst & Robot ISR P-1049001 Lisbon Portugal Inst Syst & Robot ISR Comp & Robot Vis Lab P-1049001 Lisbon Portugal Ctr Invest Naval CINAV P-2810001 Almada Portugal

In this work, we propose a new method for real-time ship segmentation during maritime surveillance missions using aircrafts with onboard video cameras. We propose a cascade model with a detection stage followed by a segmentation stage. The detection stage selects candidate regions (bounding boxes) likely to contain ships. These bounding boxes are passed to the segmentation stage, where the ship segmentation mask is then obtained. By focusing the segmentation effort only in the image regions recommended by the previous detection stage, it is possible to improve the overall image processing time and, simultaneously, to obtain a better segmentation score than the one obtained by monolithic segmentation models that are trained in an end-to-end way. Additionally, we test the viability of using a Conditional Random Field model as final boundary refinement stage: although such model can improve the segmentation results when a full segmentation approach is used, our experiments did not show any significant improvements when using our proposed cascade model. We trained the detection and segmentation models with aerial ship images from publicly available maritime datasets. We tested the cascade model on the Airbus ship detection challenge, showing real-time performance and accurate maritime ship segmentation, comparable to state-of-the-art results.

关键词： Image segmentation Marine vehicles Feature extraction Surveillance Image resolution Deep learning Computer architecture Maritime surveillance ship detection ship segmentation real-time image segmentation convolutional neural networks deep learning

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Attention-based credible evidential segmentation network for remote sensing ship segmentation

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2025年 184卷

作者： Qu, SiCong Dong, YiLin Zhu, ChangMing Cao, Lei Zuo, KeZhu Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Southeast Univ Coll Cyber Sci & Engn Nanjing 211189 Peoples R China

To address the challenges in ship segmentation arising from image quality degradation and edge blurring caused by adverse weather conditions and wave interference, this paper introduces the novel attention-based Credible Evidential Network, CEviCU-Net. Built upon Dempster-Shafer theory and the classical UNet architecture, CEviCU-Net consists of two main components: the feature extraction module and the evidential segmentation module. Specifically, the feature extraction module utilizes the U-shaped encoder-decoder framework. And this framework incorporates the Coordinate Attention Convolutional (CAC) module at the skip connection layers to extract semantic feature vector for each pixel. Subsequently, the evidential segmentation module in the CEviCU-Net employs a distance metric to compute the basic belief assignments (BBAs) between prototypes in the feature space and semantic feature vectors of each pixel. These BBAs are aggregated in the utility layer to facilitate semantic segmentation, accommodating the classification of ambiguous pixels and outliers. Finally, the model is trained end-to-end to jointly optimize all parameters of the proposed CEviCU-Net. Experimental results demonstrate the CEviCU-Net achieves the mIoU scores of 86.89% on the HRSID dataset and 90.43% on the Small shipInsSeg dataset, representing improvements of 1.97% and 3.34% in average, respectively, compared to baseline models. These findings highlight the effectiveness of our proposed CEviCUNet in the remote sensing ship segmentation.

关键词： Remote sensing ship segmentation Attention map Dempster-Shafer theory Uncertainty aware

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SwinSeg: Swin transformer and MLP hybrid network for ship segmentation in maritime surveillance system

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OCEAN ENGINEERING 2023年第1期281卷

作者： Zhang, Yuqi Li, Chaofeng Shang, Shaopeng Chen, Xinqiang Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 200135 Peoples R China Shanghai Jian Qiao Univ Vocat Coll Shanghai 201306 Peoples R China

Accurate identification and segmentation of moving ships to ensure maritime traffic safety has become an important task in maritime intelligent transportation system. With the development of artificial intelligence, maritime surveillance system based on computer vision has been widely studied. However, there are still some problems when applied to the actual maritime scene. For example, the degradation of visible image quality caused by rain, haze, and low illumination leads to a significant reduction in segmentation performance. To improve the performance of ship identification and segmentation under adverse weather conditions, we propose SwinSeg, a hybrid network combining Swin Transformer and lightweight multi-layer perceptron (MLP). To address the lack of suitable open-source datasets in the community, we have collected and labeled a semantic segmentation dataset of marine ships, named SeashipsSeg. It consists of 1200 visible marine ship images and covers six common ship types (bulk cargo carrier, container ship, fishing boat, general cargo ship, ore carrier, and passenger ship). In addition, synthetic degraded images are added to the dataset to increase its diversity and improve the generalization ability of the network. The experimental results show that the performance of our method is significantly better than the state-of-the-art (SOTA) methods in terms of segmentation accuracy, robustness, and efficiency under different weather conditions. The dataset is available at https://***/GrimreaperZ-creator/SeashipsSeg.

关键词： Maritime intelligent transportation system Maritime surveillance system ship segmentation Swin transformer Multi-layer perceptron (MLP)

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Distribution Information Based Intuitionistic Fuzzy Clustering for Infrared ship segmentation

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第8期28卷 1557-1571页

作者： Jin, Darui Bai, Xiangzhi Beijing Univ Aeronaut & Astronaut Image Proc Ctr Beijing 100191 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Adv Innovat Ctr Biomed Engn Beijing 100083 Peoples R China

This paper presents a distribution information based intuitionistic fuzzy clustering method for infrared ship segmentation. The algorithm could effectively suppress the influences of nontarget objects with high intensity and intensity inhomogeneity in the infrared ship images. There are mainly two improvements in this paper. First, it proposes a fuzzy clustering algorithm incorporating global distribution information of ship targets in the form of the Gaussian model. The spatial information, along with intensity, is used to exert different effects on different classes. Second, an intuitionistic fuzzy clustering way is incorporated into the process of ship segmentation, which combines the intensity distribution information of the local region. The intuitionistic fuzzy distance and local intensity distribution information would help in solving the problem of intensity inhomogeneity and blurring edges. Experiment results on the dataset containing 200 infrared ship images indicate the superiority of the proposed method compared with other state-of-the-art methods.

关键词： Distribution information infrared (IR) images intuitionistic fuzzy c-means (IFCM) ship segmentation

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High-resolution representations and multistage region-based network for ship detection and segmentation from optical remote sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2022年第1期16卷

作者： Huang, Bo He, Boyong Wu, Liaoni Guo, Zhiming Xiamen Univ Sch Aerosp Engn Xiamen Peoples R China

ship detection and segmentation in optical remote sensing (ORS) images is a current concern actively addressed by the academic community owing to its vast applications. Most present methods only detect the location of the ship but do not segment it at the pixel level. Considering the complex background of ORS images, identifying ships from interferences, such as clouds, waves, and some land architectures similar to ships, proves to be difficult. To address this issue, we propose a high-resolution representation and multistage region-based network (HR-MSRN) for ship detection and segmentation from ORS images. HR-MSRN mainly consists of three parts: the high-resolution feature pyramid network (HRFPN), region proposal network (RPN), and multistage detection and segmentation network (MSDSN). First, HRFPN is built as a backbone network to extract and fuse multilevel image feature maps. Second, ship candidate boxes are generated by defining numerous anchors through RPN. Third, using the idea of a cascade mask R-CNN as the reference method, the MSDSN is proposed to obtain the ship localization and mask shape. We utilize the proposed framework to evaluate an Airbus-ship dataset, and the experiments indicate that (1) HRFPN provides better feature representation ability than the ResNet-FPN when maintaining the same detection framework, especially for small ships;(2) the direct flow between mask branches refines the mask information, and the semantic segmentation branch enhances context information, which indicates that MSDSN is effective and promotes further improvements in ship detection and segmentation from ORS images;(3) in comparison to other region-based methods, HR-MSRN obtains superior performance of ship detection and segmentation in the ORS imagery. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： ship segmentation high-resolution representation cascaded network optical remote sensing

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SCV-UNET: SALIENCY-COMBINED COMPLEX-VALUED U-NET FOR SAR ship TARGET segmentation

SCV-UNET: SALIENCY-COMBINED COMPLEX-VALUED U-NET FOR SAR SHI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wei, Chenxi Ji, Zhenyuan Wei, Maosheng Zhang, Yun Yuan, Haoxuan Harbin Inst Technol Sch Elect Informat Engn Harbin Peoples R China Harbin Space Star Data Syst Technol Co Ltd Harbin Peoples R China

ISBN: (纸本)9798350320107

Since synthetic aperture radar (SAR) can observe all-weather, it is widely used in ship target detection and segmentation tasks. However, SAR images have complex backgrounds and clutter interference, which affect the segmentation accuracy. This paper proposes a saliency-combined complex-valued U-Net. The network consists of two parts, namely complexvalued U-Net(CV-UNet) and original U-Net. The CV-UNet is used to process the measured data of SAR images which contains amplitude and phase information. The original U-Net is used to process the saliency map generated by the SAR image, and the results of two parts of the network output are connected. The experiment uses the measured data of HISEA-1 to make a target segmentation dataset, and uses the trained network for testing. The results show that the performance of the proposed method is better than that of the original U-Net and CV-UNet.

关键词： ship segmentation SAR Images Complex-valued convolutional neural network (CV CNN) SCV-UNet

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Accurate ship segmentation via ship contour prediction 39

Accurate ship segmentation via ship contour prediction

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39th Chinese Control Conference (CCC)

作者： Xiao, Xiaowu Ai, Changjun Wang, Weishen Zhou, Zhiqiang Li, Linhao Chu, Jun Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Army Armament Dept Beijing Mil Representat Bur Mil Representat Off Beijing Dist 1 Beijing 100081 Peoples R China

ISBN: (纸本)9789881563903

Accurate ship segmentation in optical remote sensing images is challenging. In this paper, ship contour prediction network is introduced to improve the performance of ship segmentation network. ship contour prediction network predicts the ship contour, which can promote the learning of ship segmentation network, producing more accurate segmentation results. The ship contour prediction network can be naturally embedded into any ship segmentation network with encoder-decoder architecture. Moreover, ship contour prediction network is only used in the training process, and thus the network not only can improve segmentation accuracy, but also does not increase computational cost in the testing process.

关键词： Semantic segmentation Fully convolutional network Convolutional neural network ship segmentation ship contour prediction

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