Ship Classification with High Resolution TerraSAR-X Imagery Based on Analytic Hierarchy Process

Zhao, Zhi; Ji, Kefeng; Xing, Xiangwei; Chen, Wenting; Zou, Huanxin

doi:10.1155/2013/698370

Cited by 14 publications

(8 citation statements)

References 31 publications

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“…Due to their all-weather, all-day, and high-resolution advantages, synthetic aperture radar (SAR) images have recently been used for ship classification in marine surveillance. There are several satellites that have provided high-resolution SAR images since 2007, such as ASI’s COSMO-SkyMed, DLR’s TerraSAR-X, Japan’s ALOS-2, and China’s Gaofen-3, These high-resolution SAR images provide a resolution greater than 3 m that contain rich information about the targets, such as the geometry of ships, which makes discriminating different types of ships possible [ 1 , 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…The methods used for ship classification with SAR images mainly focus on feature selection and optimized classifier techniques [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Currently, commonly used features are (1) geometric features, such as ship length, ratio of length to width, distribution of scattering centers, covariance coefficient, contour features [ 11 ], and ship scale; and (2) scattering features, such as 2D comb features [ 7 ], local radar cross section (RCS) density [ 1 ], permanent symmetric scatterers [ 12 ], and polarimetric characteristics [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

Wang

Zhang

2018

Sensors

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

Wang

Zhang

2018

Sensors

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“…The third step, extended target identification, judging the plots of a cluster is caused by an extended target or strong clutter. And the methods proposed in [14,15] are used to distinguish ships from strong clutter. As to the last step, centroid extraction, an improved centroid extraction algorithm for autonomous star sensor is proposed in [16]; its computational complexity is lower than the algorithm proposed in [17].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Plot Fusion Method for High Resolution Radar Based on Contour Tracking Algorithm

Yan

Zhao

et al. 2016

International Journal of Antennas and Propagation

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With the development of radar system, the problem of enormous raw data has drawn much attention. A plot fusion method based on contour tracking algorithm is proposed to detect extended targets in a radar image. Firstly, the characteristic of radar image in complex environment is revealed. Then, the steps of traditional method, region growing method, and the proposed method are introduced. Meanwhile, the algorithm of tracking the contour of an extended target is illustrated in detail. It is not necessary to scan all the plots in the image, because the size of target is considered in the proposed method. Therefore, the proposed method is much more efficient than several existing methods. Lastly, the performance of several methods is tested using the raw data of two scenarios in real world. The experiment results show that the proposed method is practical and most likely to satisfy the real-time requirement in various complex environment.

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“…In such a study, an abundant of feature extraction algorithms for ship pattern analysis were proposed (Chen, 2012), followed by a novel RCS density encoding feature for ship description and a two-stage feature selection approach. A novel ship classification scheme based on analytic hierarchy process (Zhao, 2013) on both feature selection containing several evaluation measures and classification decision demonstrated good results on TerraSAR-X images. Recently (Wang, 2014), a novel hierarchical ship classifier for COSMO-SkyMed SAR data was proposed based on the analysis of geometric and backscattering characteristics of various ship types.…”

Section: Introductionmentioning

confidence: 99%

Vessel Classification in Cosmo-Skymed Sar Data Using Hierarchical Feature Selection

Makedonas

Theoharatos

Tsagaris

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

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Ship Classification with High Resolution TerraSAR-X Imagery Based on Analytic Hierarchy Process

Cited by 14 publications

References 31 publications

Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

An Efficient Plot Fusion Method for High Resolution Radar Based on Contour Tracking Algorithm

Vessel Classification in Cosmo-Skymed Sar Data Using Hierarchical Feature Selection

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