Segmentation and classification of man-made maritime objects in TerraSAR-X images

Teutsch, Michael; Saur, Günter

doi:10.1109/igarss.2011.6049749

Cited by 8 publications

(11 citation statements)

References 8 publications

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“…This classification conforms to the discrimination step of [4]. • Enhanced object segmentation and estimation of geometric parameters, and deriving a FoM, see [7]. • Extraction of the turbulent ship wake (dark tape abaft the ship's tail) in the case of presence, see [8].…”

Section: Signature Analysis and Classificationmentioning

confidence: 99%

Detection and classification of man-made offshore objects in TerraSAR-X and RapidEye imagery: Selected results of the DeMarine-DEKO project

Saur

Estable

Zielinski

et al. 2011

OCEANS 2011 IEEE - Spain

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The project DEKO (Detection of artificial objects in sea areas) is integrated in the German DeMarine-Security project and focuses on the detection and classification of ships and offshore artificial objects relying on TerraSAR-X as well as on RapidEye multispectral optical images. The objectives are 1/ the development of reliable detection algorithms and 2/ the definition of effective, customized service concepts. In addition to an earlier publication, we describe in the following paper some selected results of our work. The algorithms for TerraSAR-X have been extended to a processing chain including all needed steps for ship detection and ship signature analysis, with an emphasis on object segmentation. For Rapid Eye imagery, a ship detection algorithm has been developed. Finally, some applications are described: Ship monitoring in the Strait of Dover based on TerraSAR-X StripMap using AIS information for verification, analyzing TerraSAR-X HighResolution scenes of an industrial harbor and finally an example of surveying a wind farm using change detection

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Section: Signature Analysis and Classificationmentioning

confidence: 99%

Detection and classification of man-made offshore objects in TerraSAR-X and RapidEye imagery: Selected results of the DeMarine-DEKO project

Saur

Estable

Zielinski

et al. 2011

OCEANS 2011 IEEE - Spain

Self Cite

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“…Ship classification is mainly based on the single classifier and takes the KNN classifier as an example to illustrate the problem. It is worth noting that the feature extraction and classifier training have been widely investigated [31][32][33] and this paper will not pay additional attention to these processes.…”

Section: Classification Schemementioning

confidence: 99%

“…In this experiment, 21 features [31][32][33] were extracted from each ship from the database ( = 21). They include length 1 , width 2 , length to width ratio 3 , area 4 , perimeter 5 , shape complexity 6 , centroid 7 , moment of inertia 8 , mass 9 , the mean intensity 10 , variance coefficient 11 , weighted-rank fill ratio 12 , standard deviation 13 , fractal dimension 14 , and Hu moment 15 ∼ 21 .…”

Section: Feature Selectionmentioning

confidence: 99%

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

Zhao

Xing

et al. 2013

International Journal of Antennas and Propagation

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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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“…SVMs have been used for object classification and recognition in high-resolution SAR images [15,17]. A large number of experiments have shown that, compared with traditional neural networks, a SVM has a simpler structure, and better performance, particularly regarding its generalization ability.…”

Section: Classifiermentioning

confidence: 99%

“…In their work, silhouettes of vessels extracted from steep incidence angle SAR images were compared with silhouettes of 3-D models to discriminate vessels with comparable sizes and shapes [4]. Teutsch et al [15] presented an approach for segmentation and classification of man-made objects in TerraSAR-X images. Chen Wen-ting et al [16] proposed a novel two-stage feature selection approach for ship classification in TerraSAR-X images.…”

Section: Introductionmentioning

confidence: 99%

Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features

Wang

Jiang

et al. 2015

Remote Sensing

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Vessel monitoring is one of the most important maritime applications of Synthetic Aperture Radar (SAR) data. Because of the dihedral reflections between the vessel hull and sea surface and the trihedral reflections among superstructures, vessels usually have strong backscattering in SAR images. Furthermore, in high-resolution SAR images, detailed information on vessel structures can be observed, allowing for vessel classification in high-resolution SAR images. This paper focuses on the feature analysis of merchant vessels, including bulk carriers, container ships and oil tankers, in 3 m resolution COSMO-SkyMed stripmap HIMAGE mode images and proposes a method for vessel classification. After preprocessing, a feature vector is estimated by calculating the average value of the kernel density estimation, three structural features and the mean backscattering coefficient. Support vector machine (SVM) classifier is used for the vessel classification, and the results are compared with traditional methods, such as the K-nearest neighbor algorithm (K-NN) and minimum distance classifier (MDC). In situ investigations are conducted during the SAR data acquisition. Corresponding Automatic Identification System (AIS) reports are also obtained as ground truth to evaluate the effectiveness of the classifier. The preliminary results show that the combination of the average value of the kernel density estimation and mean backscattering coefficient has good ability for classifying the three types of vessels. When adding the three structural features, the results slightly improve. The result of the SVM classifier is better than that of K-NN and MDC. However, the SVM requires more time, when the parameters of the kernel are estimated. OPEN ACCESSRemote Sens. 2015, 7 5512

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Segmentation and classification of man-made maritime objects in TerraSAR-X images

Cited by 8 publications

References 8 publications

Detection and classification of man-made offshore objects in TerraSAR-X and RapidEye imagery: Selected results of the DeMarine-DEKO project

Detection and classification of man-made offshore objects in TerraSAR-X and RapidEye imagery: Selected results of the DeMarine-DEKO project

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

Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features

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