A Method of Ship Detection under Complex Background

He, Bin; Bi, Guoling; Zhang, Yu; Wang, Wensheng

doi:10.3390/ijgi6060159

Cited by 22 publications

(25 citation statements)

References 17 publications

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“…The texture of waves and clouds changed slowly, and that of ships varied greatly. Therefore, we introduced the histogram variance of MLBP (proposed by Section 3.1) and the correlation and contrast of the grey-level co-occurrence matrix (GLCM) [23] to strengthen the ability to distinguish ships and non-ships. The contrast and correlation of the GLCM are calculated as follows.…”

Section: Svm Trainingmentioning

confidence: 99%

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Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

Han

et al. 2020

Remote Sensing

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Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

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Section: Svm Trainingmentioning

confidence: 99%

“…Aiming at these addressed problems, a novel approach based on visual saliency and multi-dimensional descriptor is proposed. The three stages in the framework are similar to those in [23]: sea-land segmentation, ROI extraction, and target discrimination, as shown in Figure 1. However, the last two steps have been improved.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

Han

et al. 2020

Remote Sensing

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“…However, artificially designed steps result in a lack of robustness. The other methods [ 5 , 6 , 7 , 8 ] rely on machine learning to train the classifier using features extracted from positive and negative samples. When testing a new image, sliding windows or regions of interest which is produced by a pre-detection method, such as Selective Search, are sent to the classifier to predict the class score, and then, a threshold is set to obtain the final detection result.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images

Zhang

et al. 2018

Sensors

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Since remote sensing images are captured from the top of the target, such as from a satellite or plane platform, ship targets can be presented at any orientation. When detecting ship targets using horizontal bounding boxes, there will be background clutter in the box. This clutter makes it harder to detect the ship and find its precise location, especially when the targets are in close proximity or staying close to the shore. To solve these problems, this paper proposes a deep learning algorithm using a multiscale rotated bounding box to detect the ship target in a complex background and obtain the location and orientation information of the ship. When labeling the oriented targets, we use the five-parameter method to ensure that the box shape is maintained rectangular. The algorithm uses a pretrained deep network to extract features and produces two divided flow paths to output the result. One flow path predicts the target class, while the other predicts the location and angle information. In the training stage, we match the prior multiscale rotated bounding boxes to the ground-truth bounding boxes to obtain the positive sample information and use it to train the deep learning model. When matching the rotated bounding boxes, we narrow down the selection scope to reduce the amount of calculation. In the testing stage, we use the trained model to predict and obtain the final result after comparing with the score threshold and nonmaximum suppression post-processing. Experiments conducted on a remote sensing dataset show that the algorithm is robust in detecting ship targets under complex conditions, such as wave clutter background, target in close proximity, ship close to the shore, and multiscale varieties. Compared to other algorithms, our algorithm not only exhibits better performance in ship detection but also obtains the precise location and orientation information of the ship.

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“…Therefore, the false alarm rate is decreased. In recent years, visual saliency map has been applied to ship detection in SAR images [ 24 ]. In order to detect ship targets in SAR images, an optimal window selection mechanism based on the multiscale local contrast measure (LCM) is used in the local variance weighted information entropy (VWIE) [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images

Xie

Zhang

Yang

et al. 2018

Sensors

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Inshore ship detection is an important research direction of synthetic aperture radar (SAR) images. Due to the effects of speckle noise, land clutters and low signal-to-noise ratio, it is still challenging to achieve effective detection of inshore ships. To solve these issues, an inshore ship detection method based on the level set method and visual saliency is proposed in this paper. First, the image is fast initialized through down-sampling. Second, saliency map is calculated by improved local contrast measure (ILCM). Third, an improved level set method based on saliency map is proposed. The saliency map has a higher signal-to-noise ratio and the local level set method can effectively segment images with intensity inhomogeneity. In this way, the improved level set method has a better segmentation result. Then, candidate targets are obtained after the adaptive threshold. Finally, discrimination is employed to get the final result of ship targets. The experiments on a number of SAR images demonstrate that the proposed method can detect ship targets with reasonable accuracy and integrity.

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A Method of Ship Detection under Complex Background

Cited by 22 publications

References 17 publications

Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images

Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images

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