Information Theory-Based Target Detection for High-Resolution SAR Image

Liu, Shuo; Cao, Zongjie; Yang, Haiyi

doi:10.1109/lgrs.2016.2516535

Cited by 26 publications

(7 citation statements)

References 13 publications

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“…In this section, we extensively compare our method with more state-of-the-art methods on the test set, including saliency filtering [29], local contrast variance weighted information entropy (LCVIWE) [1], information theory-based target detection (ITBTD) [3] and objectness learning [54]. For our method, we set k = 15, 20.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

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Multi-Scale Proposal Generation for Ship Detection in SAR Images

et al. 2019

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The classic ship detection methods in synthetic aperture radar (SAR) images suffer from an extreme variance of ship scale. Generating a set of ship proposals before detection operation can effectively alleviate the multi-scale problem. In order to construct a scale-independent proposal generator for SAR images, we suggest four characteristics of ships in SAR images and the corresponding four procedures in this paper. Based on these characteristics and procedures, we put forward a framework to explore multi-scale ship proposals. The designed framework mainly contains two stages: hierarchical grouping and proposal scoring. Firstly, we extract edges, superpixels and strong scattering components from SAR images. The ship proposals are obtained at hierarchical grouping stage by combining the strong scattering components with superpixel grouping. Considering the difference of edge density and the completeness and tightness of contour, we obtain the scores to measure the confidence that a proposal contains a ship. Finally, the ranking proposals are obtained. Extensive experiments demonstrate the effectiveness of the four procedures. Our method achieves 0.70 the average best overlap (ABO) score, 0.59 the area under the curve (AUC) score and 0.85 best recall on a challenging dataset. In addition, the recall of our method on three scale subsets are all above 0.80. Experimental results demonstrate that our algorithm outperforms the approaches previously used for SAR images.

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Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…The detectors based on CFAR are widely used ship detection methods [2,3]. In order to improve ship detection performance, many studies attempt to modify the classic CFAR detector [4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Proposal Generation for Ship Detection in SAR Images

et al. 2019

Self Cite

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“…We can speculate that considering the cues of the region one pixel belongs to will benefit the decision of this pixel, since they may reveal the regional structural or textural difference between the target and clutter. Recent research has also shown that the superpixel can help to retain the target outline and suppress the speckle noise in target detection task [27,28]. On the other hand, since neighboring pixels often share the same label with high probability, joint sparsity prior can be adapted exactly at the superpixel-level.…”

Section: Superpixel Segmentationmentioning

confidence: 99%

Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning

et al. 2019

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Ship detection with polarimetric synthetic aperture radar (PolSAR) has received increasing attention for its wide usage in maritime applications. However, extracting discriminative features to implement ship detection is still a challenging problem. In this paper, we propose a novel ship detection method for PolSAR images via task-driven discriminative dictionary learning (TDDDL). An assumption that ship and clutter information are sparsely coded under two separate dictionaries is made. Contextual information is considered by imposing superpixel-level joint sparsity constraints. In order to amplify the discrimination of the ship and clutter, we impose incoherence constraints between the two sub-dictionaries in the objective of feature coding. The discriminative dictionary is trained jointly with a linear classifier in task-driven dictionary learning (TDDL) framework. Based on the learnt dictionary and classifier, we extract discriminative features by sparse coding, and obtain robust detection results through binary classification. Different from previous methods, our ship detection cue is obtained through active learning strategies rather than artificially designed rules, and thus, is more adaptive, effective and robust. Experiments performed on synthetic images and two RADARSAT-2 images demonstrate that our method outperforms other comparative methods. In addition, the proposed method yields better shape-preserving ability and lower computation cost.Remote Sens. 2019, 11, 769 2 of 20 two types. The first is designed by enhancing the contrast between the interested targets and clutter. Novak et al. proposed the polarimetric whitening filter (PWF) to produce a speckle-reduced image by optimally combining all the elements of the scattering matrix [3]. Yang et al. presented the generalized optimization of polarimetric contrast enhancement (GOPCE) to maximize the signal-to-clutter ratio (SCR) in the image [4]. These methods work well in high SCR condition. However, when the SCR decreased, these methods may suffer from severe performance deterioration. The other type is designed by analyzing polarimetric scattering mechanism and introducing polarimetric para-meters. Yeremy et al. implemented ship detection by using Cameron decomposition [5], while the symmetric scattering characterization method (SSCM) was developed by Touzi et al. [6]. Since the methods applied to single-look scattering matrix are generally more susceptible to the speckle and increase the probability of false alarms (PFAs) of small ship, multi-look covariance or coherency matrix-based methods have been explored further. Chen et al. introduced polarization cross entropy (PCE) based on the eigen-decomposition of polarimetric coherence matrix [7]. Moreover, degree of polarization [8] was fully investigated for ship detection. It is true that speckle is greatly reduced by spatial ensemble averaging in these methods. Nevertheless, although the scalar feature implicitly includes the contributions of all polarimetric channels, an explicit consideration of a...

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“…Both classes of algorithms are widely applied in automated driving, intelligent security, remote sensing detection and other fields. For SAR image object detection tasks, compared with traditional constant false alarm rate (CFAR) algorithms [14], [15], ship detection algorithms based on deep learning do not require complex modeling processes; consequently, they have attracted considerable research interest from scholars. Li et al applied the various training strategies to improve the Faster R-CNN detection algorithm for ship detection in SAR images [16].…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Based on an Attention Mechanism for SAR Ship Detection in Multiscale and Complex Scenarios

Chen

et al. 2019

IEEE Access

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Synthetic aperture radar (SAR) ship detection based on deep learning has been widely applied in recent years. However, two main obstacles are hindering SAR ship detection. First, the identification of ships in a port is seriously disrupted by the presence of onshore buildings. It is difficult for the existing detection algorithms to effectively distinguish the targets from such a complex background. Additionally, it appears more complicated to accurately locate densely arranged ships. Second, the ships in SAR images exist at a variety of scales due to multiresolution imaging modes and the variety of ship shapes; these pose a much greater challenge to ship detection. To solve the above problems, this paper proposes an object detection network combined with an attention mechanism to accurately locate targets in complex scenarios. To address the diverse scales of ship targets, we construct a loss function that incorporates the generalized intersection over union (GIoU) loss to reduce the scale sensitivity of the network. For the final processing of the results, soft nonmaximum suppression (Soft-NMS) is also introduced into the model to reduce the number of missed detections for ship targets in the presence of severe overlap. The experimental results reveal that the proposed model exhibits excellent performance on the extended SAR ship detection dataset (SSDD) while achieving real-time detection. INDEX TERMS Ship detection, synthetic aperture radar (SAR), deep neural network, attention mechanism.

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Information Theory-Based Target Detection for High-Resolution SAR Image

Cited by 26 publications

References 13 publications

Multi-Scale Proposal Generation for Ship Detection in SAR Images

Multi-Scale Proposal Generation for Ship Detection in SAR Images

Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning

A Deep Neural Network Based on an Attention Mechanism for SAR Ship Detection in Multiscale and Complex Scenarios

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