Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection

Kang, Miao; Ji, Kefeng; Leng, Xiangguang; Zhao, Lin

doi:10.3390/rs9080860

Cited by 270 publications

(131 citation statements)

References 28 publications

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“…Finally, the performance evaluation on real-scene data is also presented. We compare the proposed method with the iterative censoring CFAR (IC-CFAR) detector [10], Variational Bayesian Inference (VBI) [31], Superpixel-level local information measurement (SLIM) detector [32], and contextual region-based convolutional neural network with multilayer fusion (CRCNN-MF) [15]. The ground truth of the testing areas R1 and R3 are shown with Pauli vector color-coded in Figure 4.…”

Section: Experiments and Discussionmentioning

confidence: 99%

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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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Section: Experiments and Discussionmentioning

confidence: 99%

“…Thus, an index matrix can be obtained to label whether each pixel of the image is a potential target pixel or not. More parameter setting details can be found in [9,15,31,32].…”

Section: Parameter Settingmentioning

confidence: 99%

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

et al. 2019

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“…AP, PRC and mAP are three well-known and widely applied indicators to evaluate the performance of object detection methods [34]. PRC can be obtained through four evaluation components: true positive (TP), false positive (FP), false negative (FN) and true negative (TN) [35]. TP and FP indicate the number of targets detected correctly and the number of targets detected incorrectly, respectively.…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

RADet: Refine Feature Pyramid Network and Multi-Layer Attention Network for Arbitrary-Oriented Object Detection of Remote Sensing Images

Huang

Pei

et al. 2020

Remote Sensing

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Object detection has made significant progress in many real-world scenes. Despite this remarkable progress, the common use case of detection in remote sensing images remains challenging even for leading object detectors, due to the complex background, objects with arbitrary orientation, and large difference in scale of objects. In this paper, we propose a novel rotation detector for remote sensing images, mainly inspired by Mask R-CNN, namely RADet. RADet can obtain the rotation bounding box of objects with shape mask predicted by the mask branch, which is a novel, simple and effective way to get the rotation bounding box of objects. Specifically, a refine feature pyramid network is devised with an improved building block constructing top-down feature maps, to solve the problem of large difference in scales. Meanwhile, the position attention network and the channel attention network are jointly explored by modeling the spatial position dependence between global pixels and highlighting the object feature, for detecting small object surrounded by complex background. Extensive experiments on two remote sensing public datasets, DOTA and NWPUVHR -10, show our method to outperform existing leading object detectors in remote sensing field.Remote Sens. 2020, 12, 389 2 of 20 for each region of interest (RoI). Then, the features are used to achieve category-specific classification and regression for the corresponding proposals. Finally, the final detection result is obtained through post-processing, such as non-maximum suppression. Faster R-CNN is a classical two-stage object detector, which is composed of Region Proposal Networks (RPN) and a detection network consists of classifiers and regressors, and can detect objects quickly and accurately in an end-to-end manner. Based on Faster R-CNN, more improved two-stage object detectors such as Region-based Fully Convolutional Networks (R-FCN) [7] and Mask R-CNN [8] were proposed. To further improve the efficiency of the object detector, Joseph Redmon et al. proposed a single stage target detector based on regression, called YOLO [9]. For the simple structure, You Only Look Once (YOLO) is extremely fast, but its accuracy is lower than that of the two-stage detector. Based on YOLO, YOLO v3 [10] and YOLO 9000 [11] were proposed successively. To trade off the detection speed and accuracy, Single Shot MultiBox Detector (SSD) [12] was proposed, whose speed and accuracy were between YOLO series algorithm and R-CNN series algorithm.

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“…The possibilities for supplementing the system with appropriate methods to support maneuvering decisions taken in an uncertain navigational situation that occurs in a short time in relation to the greater number of objects encountered are described in [7][8][9][10][11][12]. The process of reducing the uncertainty when assessing the real navigational situation of an object by using an artificial neural network is shown in [13][14][15]. Lenart [16] proposed the parameter "time for a safe distance" after detecting dangerous objects as a potentially important parameter, accompanied by the display of possible evasive maneuvers.…”

Section: Introductionmentioning

confidence: 99%

Multistage Dynamic Optimization with Different Forms of Neural-State Constraints to Avoid Many Object Collisions Based on Radar Remote Sensing

Lisowski

2020

Remote Sensing

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This article presents the possibility of helping navigators direct the movement of an object, while safely passing through other objects, using an artificial neural network and optimization methods. It has been shown that the best trajectory of an object in terms of optimality and security, from among many possible options, can be determined by the method of dynamic programming with the simultaneous use of an artificial neural network, by depicting the encountered objects as moving in forbidden domains. Analytical considerations are illustrated with examples of simulation studies of the developed calculation program on real navigational situations at sea. This research took into account both the number of objects encountered and the different shapes of domains assigned to the objects encountered. Finally, the optimal value of the safe object trajectory time was compared on the setpoint value of the safe passing distance of objects in given visibility conditions at sea, and the degree of discretization of calculations was determined by the density of the location of nodes along the route of objects.

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Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection

Cited by 270 publications

References 28 publications

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

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

RADet: Refine Feature Pyramid Network and Multi-Layer Attention Network for Arbitrary-Oriented Object Detection of Remote Sensing Images

Multistage Dynamic Optimization with Different Forms of Neural-State Constraints to Avoid Many Object Collisions Based on Radar Remote Sensing

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