Size and Heading of SAR-Detected Ships through the Inertia Tensor

Bedini, Luigi; Righi, Marco; Salerno, Emanuele

doi:10.3390/proceedings2020097

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…A first attempt to provide information about the vessel kinematics consists of performing a refined segmentation of the candidate target and in estimating the 2D principal inertia axis of the target. Eventually, the ship's main axis is identified as the minimum inertia axis [9]. Furthermore, previous literature [16] has proven that an object moving on the water surface at constant heading and speed generates a wake pattern made up of divergent and transverse wave components (the diagram in Figure 3 shows the crests profiles for a generic wake pattern).…”

Section: Motion's Related Featuresmentioning

confidence: 97%

“…Additionally the Azimuth shift effect is also observable. The following segmentation step provides accurate estimates of the vessel centroid positioning, a binary model of the vessel shape representing the area occupied by the ship, the hull main dimensions and an estimate of the vessel orientation, which is related to the minimum inertia axis of the candidate target (see [9]). For this reason, the vessel course is provided with a 180 • ambiguity, since at this stage it is not yet possible to univocally identify the target's fore and aft.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Sensor Satellite Data Processing for Marine Traffic Understanding

Reggiannini

Bedini

2019

Electronics

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The work described in this document concerns the estimation of the kinematics of a navigating vessel. This task can be accomplished through the exploitation of satellite-borne systems for Earth observation. Indeed, Synthetic Aperture Radar (SAR) and optical sensors installed aboard satellites (European Space Agency Sentinel, ImageSat International Earth Remote Observation System, Italian Space Agency Constellation of Small Satellites for Mediterranean basin Observation) return multi-resolution maps providing information about the marine surface. A moving ship represented through satellite imaging results in a bright oblong object, with a peculiar wake pattern generated by the ship’s passage throughout the water. By employing specifically tailored computer vision methods, these vessel features can be identified and individually analyzed for what concerns geometrical and radiometric properties, backscatterers spatial distribution and the spectral content of the wake components. This paper proposes a method for the automatic detection of the vessel’s motion-related features and their exploitation to provide an estimation of the vessel velocity vector. In particular, the ship’s related wake pattern is considered as a crucial target of interest for the purposes mentioned. The corresponding wake detection module has been implemented adopting a novel approach, i.e., by introducing a specifically tailored gradient estimator in the early processing stages. This results in the enhancement of the turbulent wake detection performance. The resulting overall procedure may also be included in marine surveillance systems in charge of detecting illegal maritime traffic, combating unauthorized fishing, irregular migration and related smuggling activities.

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Section: Motion's Related Featuresmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Satellite Data Processing for Marine Traffic Understanding

Reggiannini

Bedini

2019

Electronics

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“…After thresholding, the target is isolated by clipping the image outside a mask including the ship and the possible artifacts. The subsequent step is to find the principal inertia axes by performing an eigenvalue analysis on the 2D inertia tensor [52], computed with respect to the barycenter of the thresholded image. Then, a rectangular mask is applied around the barycenter, so as to include the whole target while cutting out part of the artifacts.…”

Section: Ship Segmentationmentioning

confidence: 99%

Remote Sensing for Maritime Prompt Monitoring

Reggiannini

Righi

Tampucci

et al. 2019

JMSE

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The main purpose of this paper is to describe a software platform dedicated to sea surveillance, capable of detecting and identifying illegal maritime traffic. This platform results from the cascade pipeline of several image processing algorithms that input Radar or Optical imagery captured by satellite-borne sensors and try to identify vessel targets in the scene and provide quantitative descriptors about their shape and motion. This platform is innovative since it integrates in its architecture heterogeneous data and data processing solutions with the goal of identifying navigating vessels in a unique and completely automatic processing streamline. More in detail, the processing chain consists of: (i) the detection of target vessels in an input map; (ii) the estimation of each vessel’s most descriptive geometrical and scatterometric (for radar images) features; (iii) the estimation of the kinematics of each vessel; (iv) the prediction of each vessel’s forthcoming route; and (v) the visualization of the results in a dedicated webGIS interface. The resulting platform represents a novel tool to counteract unauthorized fishing and tackle irregular migration and the related smuggling activities.

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“…However, if ship recognition depends only on manual processing, there will be many problems, such as heavy workload, low efficiency, high repetitiveness, strong subjectivity, high cost, etc., that cannot meet the efficient information needs of modern society. Therefore, ship detection and recognition is a research hotspot in the field of image recognition [5]. In general, traditional remote sensing image ship detection methods are based on gray threshold segmentation and grayscale statistics [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

Guo

Yuan

2020

Sensors

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Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.

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Size and Heading of SAR-Detected Ships through the Inertia Tensor

Cited by 10 publications

References 7 publications

Multi-Sensor Satellite Data Processing for Marine Traffic Understanding

Multi-Sensor Satellite Data Processing for Marine Traffic Understanding

Remote Sensing for Maritime Prompt Monitoring

Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

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