Hierarchical vessel classifier based on multifeature joint matching for high-resolution inverse synthetic aperture radar images

Zhao, Haitao; Wang, Quan; Wu, Weiwei; Wang, Qingping; Shenghai, Jiao; Yuan, Naichang

doi:10.1117/1.jrs.8.083563

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…During the classification stage of an SAR ATR system, most classification methods are based on the statistical features such as the grayscales 21 and peaks, 22 while seldom considering targets' spatial structure information, such as the shape or contour that has widely been used in the target recognition of optical images. 23,24 As for SAR images, the configuration of a target and the relationship among all of its scattering centers codetermine the spatial structure of the target, and its contour may eventually embody the spatial distribution of all the pixels in the target region.…”

Section: Modelsmentioning

confidence: 99%

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Automatic target recognition scheme for a high-resolution and large-scale synthetic aperture radar image

Wang

et al. 2015

J. Appl. Remote Sens

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Existing automatic target recognition of synthetic aperture radar (SAR ATR) schemes mainly focus on target chips, but there is very little research for a large-scale and highresolution SAR image that is more practical for SAR image interpretation. How to recognize targets efficiently and accurately from a large-scale and high-resolution SAR image is still a challenge. We present a scheme based on the combination of a salient detection approach, an active contour model (ACM), an affine-invariant shape descriptor, and the corresponding shape context. During the detection stage, the spectral residual approach is utilized to efficiently preselect salient regions. The proposed convex ACM, based on a ratio distance and distribution metric which makes it more robust to multiplicative speckled noise, is then adopted to get accurate candidate target chips. For the discrimination stage, a cumulative sum of multiscale lacunarity feature is proposed to select vehicle chips from clutter chips. Finally, affine-invariant shape features, obtained from the contours by our proposed ACM, are combined with a corresponding shape context to make the classification more accurate. Experimental results demonstrate that our SAR ATR system, integrating all the proposed methods, is feasible in ATR from a high-resolution and large-scale SAR image.

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Section: Modelsmentioning

confidence: 99%

“…Based on Fig. 2,3,4,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23, and 24 chips) are assumed to be target chips. 2.2, only the No.…”

Section: Target Discriminationmentioning

confidence: 99%

Automatic target recognition scheme for a high-resolution and large-scale synthetic aperture radar image

Wang

et al. 2015

J. Appl. Remote Sens

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A novel marine radar targets extraction approach based on sequential images and Bayesian Network

Chen

Yan

et al. 2016

Ocean Engineering

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This research proposes a Bayesian Network-based methodology to extract moving vessels from a plethora of blips captured in frame-by-frame radar images. First of all, the inter-frame differences or graph characteristics of blips, such as velocity, direction, and shape, are quantified and selected as nodes to construct a Directed Acyclic Graph (DAG), which is used for reasoning the probability of a blip being a moving vessel. Particularly, an unequal-distance discretisation method is proposed to reduce the intervals of a blip's characteristics for avoiding the combinatorial explosion problem. Then, the undetermined DAG structure and parameters are learned from manually verified data samples. Finally, based on the probabilities reasoned by the DAG, judgments on blips being moving vessels are determined by an appropriate threshold on a Receiver Operating Characteristic (ROC) curve. The unique strength of the proposed methodology includes laying the foundation of targets extraction on original radar images and verified records without making any unrealistic assumptions on objects' states. A real case study has been conducted to validate the effectiveness and accuracy of the proposed methodology.

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Sentinel-1/2 Data for Ship Traffic Monitoring on the Danube River

Negula

Poenaru

Olteanu

et al. 2016

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

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ABSTRACT:After a long period of drought, the water level of the Danube River has significantly dropped especially on the Romanian sector, in July-August 2015. Danube reached the lowest water level recorded in the last 12 years, causing the blockage of the ships in the sector located close to Zimnicea Harbour. The rising sand banks in the navigable channel congested the commercial traffic for a few days with more than 100 ships involved. The monitoring of the decreasing water level and the traffic jam was performed based on Sentinel-1 and Sentinel-2 free data provided by the European Space Agency and the European Commission within the Copernicus Programme. Specific processing methods (calibration, speckle filtering, geocoding, change detection, image classification, principal component analysis, etc.) were applied in order to generate useful products that the responsible authorities could benefit from. The Sentinel data yielded good results for water mask extraction and ships detection. The analysis continued after the closure of the crisis situation when the water reached the nominal level again. The results indicate that Sentinel data can be successfully used for ship traffic monitoring, building the foundation of future endeavours for a durable monitoring of the Danube River.

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Hierarchical vessel classifier based on multifeature joint matching for high-resolution inverse synthetic aperture radar images

Cited by 3 publications

References 15 publications

Automatic target recognition scheme for a high-resolution and large-scale synthetic aperture radar image

Automatic target recognition scheme for a high-resolution and large-scale synthetic aperture radar image

A novel marine radar targets extraction approach based on sequential images and Bayesian Network

Sentinel-1/2 Data for Ship Traffic Monitoring on the Danube River

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