A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection

Lang, Haitao; Zhang, Jie; Wang, Yiduo; Zhang, Xi; Meng, Junmin

doi:10.1007/s13131-016-0924-8

Cited by 5 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The purpose of the preprocessing is to improve the detection precision while the prescreening step is used to locate candidate areas as ship region proposals. Most of the prescreening methods are based on the constant false alarm rate (CFAR) 4‐6 method which is combined with sea clutter modeling, threshold estimation and other methods to make appropriate improvements 7 . The discrimination aims at eliminating false alarms and obtaining the real targets 8,9 …”

Section: Introductionmentioning

confidence: 99%

Improved region convolutional neural network for ship detection in multiresolution synthetic aperture radar images

Xiao

Cheng

Xiao

et al. 2020

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Effectively obtaining the location and direction of the ship target is an important prerequisite for maritime traffic management and marine accident rescue. Thanks to the rapid development of the target detection methods based on deep learning, this article proposed a ship target detection method for multiresolution synthetic aperture radar (SAR) images based on improved region convolution neural network (R-CNN). According to the characteristics of ship target in the SAR images, we make several improvements such as enlarging the input, proposal optimization, database target categorization, and weight balance on the basis of the standard Faster R-CNN. The experimental results proved that the proposed method can detect target effectively and precisely in complicated scenes of multiresolution SAR images, such as inshore and dense targets. It has a good potential in practical application.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved region convolutional neural network for ship detection in multiresolution synthetic aperture radar images

Xiao

Cheng

Xiao

et al. 2020

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…The result more accurately fits the different sea backgrounds. Lang [ 18 ] proposed a novel nonparametric sea background distribution estimation method based on an n-order Bézier curve. The proposed method is as good as a traditional nonparametric Parzen window kernel method.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ship Detection Method Based on Gradient and Integral Feature for Single-Polarization Synthetic Aperture Radar Imagery

Shi

Zhang

Bian

et al. 2018

Sensors

View full text Add to dashboard Cite

With the rapid development of remote sensing technologies, SAR satellites like China’s Gaofen-3 satellite have more imaging modes and higher resolution. With the availability of high-resolution SAR images, automatic ship target detection has become an important topic in maritime research. In this paper, a novel ship detection method based on gradient and integral features is proposed. This method is mainly composed of three steps. First, in the preprocessing step, a filter is employed to smooth the clutters and the smoothing effect can be adaptive adjusted according to the statistics information of the sub-window. Thus, it can retain details while achieving noise suppression. Second, in the candidate area extraction, a sea-land segmentation method based on gradient enhancement is presented. The integral image method is employed to accelerate computation. Finally, in the ship target identification step, a feature extraction strategy based on Haar-like gradient information and a Radon transform is proposed. This strategy decreases the number of templates found in traditional Haar-like methods. Experiments were performed using Gaofen-3 single-polarization SAR images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency. In addition, this method has the potential for on-board processing.

show abstract

Ship Detection Algorithm for SAR Images Based on Lightweight Convolutional Network

Wang

Shi

Chen

2022

J Indian Soc Remote Sens

View full text Add to dashboard Cite

A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection

Cited by 5 publications

References 24 publications

Improved region convolutional neural network for ship detection in multiresolution synthetic aperture radar images

Improved region convolutional neural network for ship detection in multiresolution synthetic aperture radar images

A Novel Ship Detection Method Based on Gradient and Integral Feature for Single-Polarization Synthetic Aperture Radar Imagery

Ship Detection Algorithm for SAR Images Based on Lightweight Convolutional Network

Contact Info

Product

Resources

About