Comparison of ship detectors for polarimetric SAR imagery

Crisp, David J.; Keevers, Thomas

doi:10.1109/oceanssyd.2010.5603581

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…Since some form of CFAR detection seems to be a de facto standard for prescreening, here we consider everything capable of refining its output as a discrimination algorithm. The literature often refers to either polarimetry or interferometry to perform discrimination [7][8][9]. In addition, for polarimetric data analysis, a segmentation step is recommended by some authors [10][11][12].…”

Section: Ship Detection and 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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Section: Ship Detection and Segmentationmentioning

confidence: 99%

Remote Sensing for Maritime Prompt Monitoring

Reggiannini

Righi

Tampucci

et al. 2019

JMSE

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“…The extrema of F are determined using a Lagrangian multiplier α. y 1 , y 2 and y 3 are then derived as a function of α and replaced in the constraint (20). This leads to a six-order polynomial equation α, which can be numerically solved.…”

Section: Appendixmentioning

confidence: 99%

“…To reduce speckle effects, local averaging of the covariance (Mueller or coherency) matrix can be performed using weighting related to the local scene homogeneity [9], [31], [32]. Since the averaging process should be carried out on the covariance (or Mueller) matrix to preserve polarimetric information [9], [33], many algorithms have been introduced for ship detection using the multilook covariance (or coherency) matrix [11], [20], [21], [23], [34]. Several techniques, which use the parameters provided by the characteristic decomposition of the covariance (or coherency) matrix, led to interesting results [11], [20], [22], [23], [34], [35].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Degree of Polarization for Enhanced Ship Detection Using Polarimetric RADARSAT-2

Touzi

Hurley²,

Vachon³

2015

IEEE Trans. Geosci. Remote Sensing

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The scattered wave is represented in terms of two independent and rotation invariant parameters: the degree of polarization (DoP) and the total scattered intensity R 0 . The scattered wave polarization signature is introduced as a convenient graphical representation of the variations of the two scattered wave observable parameters as a function of the transmitting antenna polarization. It is shown that the signature of the DoP and the total scattered intensity (R 0 ) provide information on ocean and ship scattering that is complementary to that provided by the conventional received wave intensity polarization signature. While R 0 polarization signature of the ocean's Bragg scattering is sensitive to wind speed and synthetic aperture radar illumination angle, the DoP polarization signature appears to depend mainly on ocean surface roughness. The large dynamic range and low pedestal height of ship DoP polarization signature, in comparison with that of the ocean, favors the use of DoP for ship detection in comparison with R 0 . Optimization of the scattered wave parameters appears to be a convenient tool for efficient ship detection. The scattered wave optimization technique introduced by R. Touzi et al. in the nineties, is reconsidered and applied for enhanced ship detection. It is shown that the excursion of DoP, Δp, and the minimum DoP, p min , permits a significant improvement in ship-sea contrast in comparison with conventional (i.e., scalar) single channel polarizations (HH, VV, and HV). The additional information provided by the maximum DoP, p max solves for ship ambiguities with land targets. Quantification of the ship-ocean contrast is reconsidered in the context of nonstationary ship signal. The ship peak signal quantification appears to be the most suitable method for accurate measurement of ship-ocean contrast in the presence of a nonstationary ship signal. The local p min performs better than the peak of Δp and single polarizations (HH, VV, and HV). The added value of polarimetric RS2 information for ship detection is demonstrated using wide swath (50 km) polarimetric RADARSAT-2 data collected at 29 • and 40 • incidence angle over vessels (validated with Automatic Identification System data) in the Strait of Georgia, near Vancouver, Canada.Index Terms-Clutter estimation, ocean wave, satellite applications, ship detection, synthetic aperture radar (SAR), radar polarimetry.

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“…Thus, computer aided detection system will be efficient in identification of such anomalies. In case of military, automatic ship detection helps in enhancement of maritime security through Intelligence, Surveillance and Reconnaissance (ISR) efforts [5]. A wide range of functionaries in the shipping industry encompasses the defense of territory and naval battles, dynamic harbor surveillance, monitoring of traffic and sea pollution, management of fisheries, etc.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

Roy¹,

Chaudhuri²,

Pramanik³

et al. 2023

Computer Systems Science and Engineering

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In recent years, computer vision finds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture. Automatic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies. Waterways being an important medium of transport require continuous monitoring for protection of national security. The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea. This paper proposes a deep learning based model capable enough to classify between ships and noships as well as to localize ships in the original images using bounding box technique. Furthermore, classified ships are again segmented with deep learning based auto-encoder model. The proposed model, in terms of classification, provides successful results generating 99.5% and 99.2% validation and training accuracy respectively. The auto-encoder model also produces 85.1% and 84.2% validation and training accuracies. Moreover the IoU metric of the segmented images is found to be of 0.77 value. The experimental results reveal that the model is accurate and can be implemented for automatic ship detection in water bodies considering remote sensing satellite images as input to the computer vision system.

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Comparison of ship detectors for polarimetric SAR imagery

Cited by 11 publications

References 10 publications

Remote Sensing for Maritime Prompt Monitoring

Remote Sensing for Maritime Prompt Monitoring

Optimization of the Degree of Polarization for Enhanced Ship Detection Using Polarimetric RADARSAT-2

Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

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