The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Haitao Lang - One of the best experts on this subject based on the ideXlab platform.
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distribution discrepancy maximization metric learning for Ship Classification in synthetic aperture radar images
International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Haitao Lang, Xiaopeng ChaiAbstract:Supervised learning techniques are widely used in the task of Ship Classification in synthetic aperture radar (SAR) images in recent years. Learning distance metrics that describe the underlying distribution between data points based on the distance metric learning (DML) methods can further improve the performance of Ship Classification in SAR images. Traditional supervised DML methods usually learn distance metrics based on pairwise constraints, but ignore the importance of inter-class distribution discrepancy. In this study, we propose a novel DML method named distribution discrepancy maximization metric learning (DDMML) algorithm, which maximizes the maximum mean discrepancy (MMD) between different categories in the process of learning distance metrics. We adopt a high-resolution SAR Ship database for experimental evaluation. The experimental results show that the proposed method outperforms the state-of-the-art DML methods.
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discriminative adaptation regularization framework based transfer learning for Ship Classification in sar images
IEEE Geoscience and Remote Sensing Letters, 2019Co-Authors: Haitao Lang, Lihui NiuAbstract:Ship Classification in synthetic-aperture radar (SAR) images is of great significance for dealing with various marine matters. Although traditional supervised learning methods have recently achieved dramatic successes, but they are limited by the insufficient labeled training data. This letter presents a novel unsupervised domain adaptation (DA) method, termed as discriminative adaptation regularization framework-based transfer learning (D-ARTL), to address the problem in case that there is no labeled training data available at all in the SAR image domain, i.e., target domain (TD). D-ARTL improves the original ARTL by adding a novel source discriminative information preservation (SDIP) regularization term. This improvement achieves an efficient transfer of interclass discriminative ability from source domain (SD) to TD, while achieving the alignment of cross-domain distributions. Extensive experiments have verified that D-ARTL outperforms state-of-the-art methods on the task of Ship Classification in SAR images by transferring the automatic identification system (AIS) information.
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Distance metric learning for Ship Classification in SAR images
Image and Signal Processing for Remote Sensing XXIV, 2018Co-Authors: Haitao Lang, Xiaopeng ChaiAbstract:Synthetic aperture radar (SAR) Ship image Classification is of great significance in the field of marine Ship monitoring. Extracting effective feature representation and constructing suitable classifier can fundamentally improve the accuracy of Ship Classification. At present, using distance metric learning (DML) algorithm to learn effective distance metrics for classifiers has been widely used in information retrieval and face recognition, but its ability to implement SAR Ship image Classification is still unknown. In this paper, we show the performance of 4 feature representations and 20 DML algorithms in SAR Ship Classification. Experimental results show that extracting effective feature representation is essential, and the DML algorithm has the ability to learn better distance metrics.
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multiple features learning for Ship Classification in optical imagery
Multimedia Tools and Applications, 2018Co-Authors: Longhui Huang, Chen Chen, Fan Zhang, Haitao LangAbstract:The sea surface vessel/Ship Classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since Ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make Ship Classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for Ship Classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final Classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing Ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods.
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Ship Classification in sar images improved by ais knowledge transfer
IEEE Geoscience and Remote Sensing Letters, 2018Co-Authors: Haitao LangAbstract:A major bottleneck in limiting the application of the existing methods of Ship Classification in synthetic aperture radar (SAR) images is the inadequate amount of labeled data available for training a classifier. However, generating ground truth involves expensive and time-consuming ground campaigns or is costly, since a high number of SAR image acquisition will be necessary. In contrast, an automatic identification system (AIS), which is an automatic tracking system used for monitoring maritime Ships, can provide plenty of labeled Ship samples that is relatively easier to be obtained. Inspired by these facts, this letter proposes to improve Ship Classification in SAR images by transferring AIS knowledge. We propose an improved multiclass adaptive support vector machine, combined with the naive geometric features (NGFs), to achieve transfer learning between the AIS domain and the SAR image domain. The experiments prove that the traditional method can be significantly improved by AIS information transfer, especially when only a few training samples in the SAR domain are available. In addition, it also shows that after feature selection, the performance of the proposed method can be close to that of the state of the art, even if by only using simpler NGFs and few training samples.
Amitrajeet A Batabyal - One of the best experts on this subject based on the ideXlab platform.
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two aspects of alien species management in a seaport Ship Classification and Ship inspection costs
Social Science Research Network, 2008Co-Authors: Amitrajeet A BatabyalAbstract:The many aspects of alien species management normally all involve decision making over time and under uncertainty. Therefore, in this note, we focus on an arbitrary seaport in a country called Home and we conduct a dynamic and stochastic analysis of two questions that have received insufficient attention in the extant literature on alien species management. First, we provide a particular way of classifying Ships that enter the Home seaport from K possible countries in the time interval [0,t]. Second, we characterize the total cost of inspecting the Ships that arrive in the Home seaport during the same time interval [0,t].
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two aspects of alien species management in a seaport Ship Classification and Ship inspection costs
Studies in Regional Science, 2008Co-Authors: Amitrajeet A BatabyalAbstract:The many aspects of alien species management normally all involve decision making over time and under uncertainty. Therefore, in this note, we focus on an arbitrary seaport in a country called Home and we conduct a dynamic and stochastic analysis of two questions that have received insufficient attention in the extant literature on alien species management. First, we provide a particular way of classifying Ships that enter the Home seaport from K possible countries in the time interval [0, t]. Second, we characterize the total cost of inspecting the Ships that arrive in the Home seaport during the same time interval [0, t].JEL Classification: F180, Q200, Q560
Xiangwei Xing - One of the best experts on this subject based on the ideXlab platform.
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2D comb feature for analysis of Ship Classification in high-resolution SAR imagery
Electronics Letters, 2017Co-Authors: Xiangguang Leng, Kefeng Ji, Shilin Zhou, Xiangwei XingAbstract:A new feature named `2D comb' to improve Ship Classification is proposed. The proposed feature presents added value to distinguish between container Ship, tank Ship and cargo Ship. It is based on radar cross-section (RCS) statistic of the Ship target related to the Ship structure. Besides, related local RCS is proposed to classify three kinds of Ships. Experimental results based on TerraSAR-X images show that the proposed feature can abstract and describe the Ship structure in high-resolution synthetic aperture radar (SAR) imagery. It establishes relationShip between RCS and Ship structure, which is very useful to distinguish different kinds of Ships.
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A comb feature for the analysis of Ship Classification in high resolution SAR imagery
2016 CIE International Conference on Radar (RADAR), 2016Co-Authors: Xiangguang Leng, Kefeng Ji, Shilin Zhou, Xiangwei XingAbstract:In the context of maritime surveillance from high resolution SAR imagery, this paper illustrates a study of Ship Classification, together with a new feature named `comb' suggested to solve the problem. The proposed comb feature presents the added value in application to distinguish between container Ship, tank Ship, and cargo Ship. It is based on the analysis on radar cross section (RCS) statistic of the Ship target related to the Ship structure. Besides, a related local RCS (LRCS) is proposed to classify three kinds of Ships. The data for experiments is made of TerraSAR-X images.
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Ship Classification with high resolution terrasar x imagery based on analytic hierarchy process
International Journal of Antennas and Propagation, 2013Co-Authors: Zhi Zhao, Kefeng Ji, Xiangwei Xing, Wenting ChenAbstract:Ship surveillance using space-borne synthetic aperture radar (SAR), taking advantages of high resolution over wide swaths and all-weather working capability, has attracted worldwide attention. Recent activity in this field has concentrated mainly on the study of Ship detection, but the Classification is largely still open. In this paper, we propose a novel Ship Classification scheme based on analytic hierarchy process (AHP) in order to achieve better performance. The main idea is to apply AHP on both feature selection and Classification decision. On one hand, the AHP based feature selection constructs a selection decision problem based on several feature evaluation measures (e.g., discriminability, stability, and information measure) and provides objective criteria to make comprehensive decisions for their combinations quantitatively. On the other hand, we take the selected feature sets as the input of KNN classifiers and fuse the multiple Classification results based on AHP, in which the feature sets’ confidence is taken into account when the AHP based Classification decision is made. We analyze the proposed Classification scheme and demonstrate its results on a Ship dataset that comes from TerraSAR-X SAR images.
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Ship Classification in terrasar x sar images based on classifier combination
International Geoscience and Remote Sensing Symposium, 2013Co-Authors: Xiangwei Xing, Huanxin Zou, Wenting Chen, Junli ChenAbstract:Ship Classification is an important step in maritime surveillance utilizing synthetic aperture radar images. In this paper, we focus on the classifier architecture. The paper investigates three individual classifiers, i.e., the K nearest neighbor classifier, the Bayes classifier, and the back-propagation neural network classifier from the viewpoint of discrimination measurements firstly. Then, we propose a SVM combination strategy to fuse the results of individual classifiers. Extensive experiments conducted on the TerraSAR-X SAR images validate the effectiveness of the proposed method.
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Ship Classification in terrasar x images with feature space based sparse representation
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Xiangwei Xing, Huanxin Zou, Wenting Chen, Jixiang SunAbstract:Ship Classification is the key step in maritime surveillance using synthetic aperture radar (SAR) imagery. In this letter, we develop a new Ship Classification method in TerraSAR-X images based on sparse representation in feature space, in which the sparse representation Classification (SRC) method is exploited. In particular, to describe the Ship more accurately and to reduce the dimension of the dictionary in SRC, we propose to employ a representative feature vector to construct the dictionary instead of utilizing the image pixels directly. By testing on a Ship data set collected from TerraSAR-X images, we show that the proposed method is superior to traditional methods such as the template matching (TM), K-nearest neighbor (K-NN), Bayes and Support Vector Machines (SVM).
Hong Zhang - One of the best experts on this subject based on the ideXlab platform.
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fine grained Ship Classification based on deep residual learning for high resolution sar images
Remote Sensing Letters, 2019Co-Authors: Yingbo Dong, Hong Zhang, Chao Wang, Yuanyuan WangAbstract:As the resolution of Synthetic Aperture Radar (SAR) images increases, the fine-grained Classification of Ships has become a focus of the SAR field. In this paper, a Ship Classification framework ba...
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Impact Analysis of Incident Angle Factor on High-Resolution Sar Image Ship Classification Based on Deep Learning
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Yingbo Dong, Yuanyuan Wang, Chao Wang, Hong Zhang, Bo ZhangAbstract:In this paper, a Ship Classification framework based on deep learning is proposed. We focus on the influence of the incident angle factor for the Classification results on deep learning-based methods. A representative SAR Ship dataset containing three types of Ship and the coverage of incidence angle is approximately from 20° to 60° is created. We evaluated the training-test performance of four deep learning models on the dataset. Taking cargo Ship as the example, the experimental results show that when using data with different range of incident angles for training, the Classification performance on test set with different range of incident angles varies greatly. The first analysis of the incident angle factor in SAR Ship Classification using deep learning methods allowed researchers to select appropriate data when using the deep learning method to classify Ships in SAR images, and may suggest satellite parameters based on the Classification results.
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Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery
MDPI AG, 2019Co-Authors: Yuanyuan Wang, Chao Wang, Hong Zhang, Yingbo Dong, Sisi WeiAbstract:Independent of daylight and weather conditions, synthetic aperture radar (SAR) imagery is widely applied to detect Ships in marine surveillance. The shapes of Ships are multi-scale in SAR imagery due to multi-resolution imaging modes and their various shapes. Conventional Ship detection methods are highly dependent on the statistical models of sea clutter or the extracted features, and their robustness need to be strengthened. Being an automatic learning representation, the RetinaNet object detector, one kind of deep learning model, is proposed to crack this obstacle. Firstly, feature pyramid networks (FPN) are used to extract multi-scale features for both Ship Classification and location. Then, focal loss is used to address the class imbalance and to increase the importance of the hard examples during training. There are 86 scenes of Chinese Gaofen-3 Imagery at four resolutions, i.e., 3 m, 5 m, 8 m, and 10 m, used to evaluate our approach. Two Gaofen-3 images and one Constellation of Small Satellite for Mediterranean basin Observation (Cosmo-SkyMed) image are used to evaluate the robustness. The experimental results reveal that (1) RetinaNet not only can efficiently detect multi-scale Ships but also has a high detection accuracy; (2) compared with other object detectors, RetinaNet achieves more than a 96% mean average precision (mAP). These results demonstrate the effectiveness of our proposed method
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Ship Classification in high resolution sar images using deep learning of small datasets
Sensors, 2018Co-Authors: Yuanyuan Wang, Chao Wang, Hong ZhangAbstract:With the capability to automatically learn discriminative features, deep learning has experienced great success in natural images but has rarely been explored for Ship Classification in high-resolution SAR images due to the training bottleneck caused by the small datasets. In this paper, convolutional neural networks (CNNs) are applied to Ship Classification by using SAR images with the small datasets. First, Ship chips are constructed from high-resolution SAR images and split into training and validation datasets. Second, a Ship Classification model is constructed based on very deep convolutional networks (VGG). Then, VGG is pretrained via ImageNet, and fine tuning is utilized to train our model. Six scenes of COSMO-SkyMed images are used to evaluate our proposed model with regard to the Classification accuracy. The experimental results reveal that (1) our proposed Ship Classification model trained by fine tuning achieves more than 95% average Classification accuracy, even with 5-cross validation; (2) compared with other models, the Ship Classification model based on VGG16 achieves at least 2% higher accuracies for Classification. These experimental results reveal the effectiveness of our proposed method.
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Ship Classification with deep learning using cosmo skymed sar data
International Geoscience and Remote Sensing Symposium, 2017Co-Authors: Chao Wang, Hong Zhang, Bo Zhang, Sirui TianAbstract:Ship Classification with spaceborne high resolution synthetic aperture radar (SAR) has wide applications in maritime traffic monitoring, fishing law-enforcement operation, marine security, etc. Deep learning, which has the ability of learning features itself, is successfully used in computer vision and artificial intelligence, and introduced into remote sensing field in recent years. In this study, the Italian COSMO-SkyMed SAR images acquired on Jul. 12–15, 2010 were used for Ship Classification with convolution neural networks in the Google's TensorFlow environment. The results show that cargo Ships could be discriminated from non-cargo Ships. Due to variations of radar illumination directions and Ship poses, more data are necessary for sub-category Classification.
Chao Wang - One of the best experts on this subject based on the ideXlab platform.
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fine grained Ship Classification based on deep residual learning for high resolution sar images
Remote Sensing Letters, 2019Co-Authors: Yingbo Dong, Hong Zhang, Chao Wang, Yuanyuan WangAbstract:As the resolution of Synthetic Aperture Radar (SAR) images increases, the fine-grained Classification of Ships has become a focus of the SAR field. In this paper, a Ship Classification framework ba...
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Impact Analysis of Incident Angle Factor on High-Resolution Sar Image Ship Classification Based on Deep Learning
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Yingbo Dong, Yuanyuan Wang, Chao Wang, Hong Zhang, Bo ZhangAbstract:In this paper, a Ship Classification framework based on deep learning is proposed. We focus on the influence of the incident angle factor for the Classification results on deep learning-based methods. A representative SAR Ship dataset containing three types of Ship and the coverage of incidence angle is approximately from 20° to 60° is created. We evaluated the training-test performance of four deep learning models on the dataset. Taking cargo Ship as the example, the experimental results show that when using data with different range of incident angles for training, the Classification performance on test set with different range of incident angles varies greatly. The first analysis of the incident angle factor in SAR Ship Classification using deep learning methods allowed researchers to select appropriate data when using the deep learning method to classify Ships in SAR images, and may suggest satellite parameters based on the Classification results.
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Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery
MDPI AG, 2019Co-Authors: Yuanyuan Wang, Chao Wang, Hong Zhang, Yingbo Dong, Sisi WeiAbstract:Independent of daylight and weather conditions, synthetic aperture radar (SAR) imagery is widely applied to detect Ships in marine surveillance. The shapes of Ships are multi-scale in SAR imagery due to multi-resolution imaging modes and their various shapes. Conventional Ship detection methods are highly dependent on the statistical models of sea clutter or the extracted features, and their robustness need to be strengthened. Being an automatic learning representation, the RetinaNet object detector, one kind of deep learning model, is proposed to crack this obstacle. Firstly, feature pyramid networks (FPN) are used to extract multi-scale features for both Ship Classification and location. Then, focal loss is used to address the class imbalance and to increase the importance of the hard examples during training. There are 86 scenes of Chinese Gaofen-3 Imagery at four resolutions, i.e., 3 m, 5 m, 8 m, and 10 m, used to evaluate our approach. Two Gaofen-3 images and one Constellation of Small Satellite for Mediterranean basin Observation (Cosmo-SkyMed) image are used to evaluate the robustness. The experimental results reveal that (1) RetinaNet not only can efficiently detect multi-scale Ships but also has a high detection accuracy; (2) compared with other object detectors, RetinaNet achieves more than a 96% mean average precision (mAP). These results demonstrate the effectiveness of our proposed method
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Ship Classification in high resolution sar images using deep learning of small datasets
Sensors, 2018Co-Authors: Yuanyuan Wang, Chao Wang, Hong ZhangAbstract:With the capability to automatically learn discriminative features, deep learning has experienced great success in natural images but has rarely been explored for Ship Classification in high-resolution SAR images due to the training bottleneck caused by the small datasets. In this paper, convolutional neural networks (CNNs) are applied to Ship Classification by using SAR images with the small datasets. First, Ship chips are constructed from high-resolution SAR images and split into training and validation datasets. Second, a Ship Classification model is constructed based on very deep convolutional networks (VGG). Then, VGG is pretrained via ImageNet, and fine tuning is utilized to train our model. Six scenes of COSMO-SkyMed images are used to evaluate our proposed model with regard to the Classification accuracy. The experimental results reveal that (1) our proposed Ship Classification model trained by fine tuning achieves more than 95% average Classification accuracy, even with 5-cross validation; (2) compared with other models, the Ship Classification model based on VGG16 achieves at least 2% higher accuracies for Classification. These experimental results reveal the effectiveness of our proposed method.
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Ship Classification with deep learning using cosmo skymed sar data
International Geoscience and Remote Sensing Symposium, 2017Co-Authors: Chao Wang, Hong Zhang, Bo Zhang, Sirui TianAbstract:Ship Classification with spaceborne high resolution synthetic aperture radar (SAR) has wide applications in maritime traffic monitoring, fishing law-enforcement operation, marine security, etc. Deep learning, which has the ability of learning features itself, is successfully used in computer vision and artificial intelligence, and introduced into remote sensing field in recent years. In this study, the Italian COSMO-SkyMed SAR images acquired on Jul. 12–15, 2010 were used for Ship Classification with convolution neural networks in the Google's TensorFlow environment. The results show that cargo Ships could be discriminated from non-cargo Ships. Due to variations of radar illumination directions and Ship poses, more data are necessary for sub-category Classification.