Automatic moving targets detection using a rule-based system: comparison between different study cases

Ferrara, M.N.; Torre, Alberto Della

doi:10.1109/igarss.1998.691633

Cited by 9 publications

(14 citation statements)

References 5 publications

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“…In a previous work [3] has been presented as application the ships and wakes detection. Since the parameterisation is a difficult point in this kind of systems, we perform some tests, particularly we evaluate the probabilities of detection and test the rules parameterisation, using different images with different geometric resolutions [4]. The developed demonstrator implements a set of rules in a hierarchical order, that is all the pixels or the objects discarded in a previous step will be not considered in the following.…”

Section: Demonstrator Architecture Evolutionmentioning

confidence: 99%

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<title>Improvement in automatic detection and recognition of moving targets in Alenia Aerospazio activity</title>

1998

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In Alenia Aerospazio has been developed a demonstrator for SAR data analysis and automatic target recognition (AIR). The new version we present in this paper introduces some new characteristics.A better sidelobes suppression via the locally variant apodization and a new CFAR processor using a cell-average or an ordered-statistics detector are integrated to the previous structure to improve the preliminary target detection i.e. the candidate ship detection.After this step and in parallel with the ship wake detection we implement a new and independent way to recognise moving objects with a following improvement in detection performances: the estimation of residual coherent phase error locally present on the focused image, due to the target motion. The test images are referred to the SIR-C/X-SAR mission.

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Section: Demonstrator Architecture Evolutionmentioning

confidence: 99%

“…Since last year the developed SW has been tested on different images acquired at different time and having different resolution (multilook images) [4]. From the analysis of the results the SW has shown some weak points especially in the ship like pixels detection.…”

Section: Introductionmentioning

confidence: 98%

<title>Improvement in automatic detection and recognition of moving targets in Alenia Aerospazio activity</title>

1998

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“…Most ship detection systems require land masking, where the land areas are omitted from the images being processed. Employing registration [6] and coastline detection methods [7], land masking is intended to eliminate the adverse effect caused by land. Many preprocessing methods, such as speckle filtering, aim to improve the facility and accuracy of subsequent stages.…”

Section: Introductionmentioning

confidence: 99%

A coupled convolutional neural network for small and densely clustered ship detection in SAR images

Zhao

Guo

Zhang

et al. 2018

Sci. China Inf. Sci.

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Ship detection from synthetic aperture radar (SAR) imagery plays a significant role in global marine surveillance. However, a desirable performance is rarely achieved when detecting small and densely clustered ship targets, and this problem is difficult to solve. Recently, convolutional neural networks (CNNs) have shown strong detection power in computer vision and are flexible in complex background conditions, whereas traditional methods have limited ability. However, CNNs struggle to detect small targets and densely clustered ones that exist widely in many SAR images. To address this problem while preserving the good properties for complex background conditions, we develop a coupled CNN for small and densely clustered SAR ship detection. The proposed method mainly consists of two subnetworks: an exhaustive ship proposal network (ESPN) for ship-like region generation from multiple layers with multiple receptive fields, and an accurate ship discrimination network (ASDN) for false alarm elimination by referring to the context information of each proposal generated by ESPN. The motivation in ESPN is to generate as many ship proposals as possible, and in ASDN, the goal is to obtain the final results accurately. Experiments are evaluated on two data sets. One is collected from 60 wide-swath Sentinel-1 images and the other is from 20 GaoFen-3 (GF-3) images. Both data sets contain many ships that are small and densely clustered. The quantitative comparison results illustrate the clear improvements of the new method in terms of average precision (AP) and F 1 score by 0.4028 and 0.3045 for the Sentinel-1 data set compared with the multi-step constant false alarm rate (CFAR-MS) method. The values are verified as 0.2033 and 0.1522 for the GF-3 data set. In addition, the new method is demonstrated to be more efficient than CFAR-MS.

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“…The speckle filters are recommended as a preprocessing step by Ferrara et al in their ship detection work [4,5]. Prescreening, also known as the detection step, is the core of the ship detection algorithm, and constant false alarm rate (CFAR)-based methods are the most widely used approaches for the detection phase.…”

Section: Introductionmentioning

confidence: 99%

Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

Pan

Liu

Qiu

et al. 2017

Sensors

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This study aims to detect vessels with lengths ranging from about 70 to 300 m, in Gaofen-3 (GF-3) SAR images with ultrafine strip-map (UFS) mode as fast as possible. Based on the analysis of the characteristics of vessels in GF-3 SAR imagery, an effective vessel detection method is proposed in this paper. Firstly, the iterative constant false alarm rate (CFAR) method is employed to detect the potential ship pixels. Secondly, the mean-shift operation is applied on each potential ship pixel to identify the candidate target region. During the mean-shift process, we maintain a selection matrix recording which pixels can be taken, and these pixels are called as the valid points of the candidate target. The l1 norm regression is used to extract the principal axis and detect the valid points. Finally, two kinds of false alarms, the bright line and the azimuth ambiguity, are removed by comparing the valid area of the candidate target with a pre-defined value and computing the displacement between the true target and the corresponding replicas respectively. Experimental results on three GF-3 SAR images with UFS mode demonstrate the effectiveness and efficiency of the proposed method.

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Automatic moving targets detection using a rule-based system: comparison between different study cases

Cited by 9 publications

References 5 publications

<title>Improvement in automatic detection and recognition of moving targets in Alenia Aerospazio activity</title>

<title>Improvement in automatic detection and recognition of moving targets in Alenia Aerospazio activity</title>

A coupled convolutional neural network for small and densely clustered ship detection in SAR images

Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

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