scholarly journals Detection and Recognition of SAR Small Ship Objects Using Deep Neural Network

2019 ◽  
Vol 37 (3) ◽  
pp. 587-593
Author(s):  
Songjie Wei ◽  
Pengfei Jiang ◽  
Qiuzhuang Yuan ◽  
Meilin Liu
Keyword(s):  
Object Detection ◽  
Target Detection ◽  
Recognition Performance ◽  
Learning Technology ◽  
Small Target ◽  
Sar Images ◽  
Small Target Detection ◽  
Detection Model ◽  
Small Ship ◽  
Detection And Recognition

Synthetic aperture radar(SAR) ship target detection plays an increasingly important role in marine monitoring. Aimed at the problems of recognizing small size of ship targets in SAR images and the inability of traditional methods to extract fine target features due to external disturbances, we propose an improved SAR small target detection model based on the deep learning technology. The proposed model mainly consists of two parts:region proposal network(RPN) and object detection network. Firstly, a CNN model is designed and trained to accurately identify small ship targets. Then, the model is used to initialize the parameters of the shared feature extraction layer. Last, we train the proposed object detection model using a self-collected Sentinel-1 SAR small target dataset. The experimental results show that the proposed target detection model has better detection and recognition performance and anti-interference ability for small ship scalable targets in SAR images, and has certain reference value for the research of small target detection in SAR images.

scholarly journals Small-Target Detection between SAR Images Based on Statistical Modeling of Log-Ratio Operator

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1431
Author(s):  
Chao Chen ◽  
Kuihua Huang ◽  
Gui Gao
Keyword(s):  
Change Detection ◽  
Target Detection ◽  
Small Target ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Ml Estimation ◽  
Small Target Detection ◽  
Multi Temporal ◽  
Lr Operator ◽  
Log Ratio

The log-ratio (LR) operator is well suited for change detection in synthetic aperture radar (SAR) amplitude or intensity images. In applying the LR operator to change detection in multi-temporal SAR images, a crucial problem is how to develop precise models for the LR statistics. In this study, we first derive analytically the probability density function (PDF) of the LR operator. Subsequently, the PDF of the LR statistics is parameterized by three parameters, i.e., the number of looks, the coherence magnitude, and the true intensity ratio. Then, the maximum-likelihood (ML) estimates of parameters in the LR PDF are also derived. As an example, the proposed statistical model and corresponding ML estimation are used in an operational application, i.e., determining the constant false alarm rate (CFAR) detection thresholds for small target detection between SAR images. The effectiveness of the proposed model and corresponding ML estimation are verified by applying them to measured multi-temporal SAR images, and comparing the results to the well-known generalized Gaussian (GG) distribution; the usefulness of the proposed LR PDF for small target detection is also shown.

scholarly journals Infrared small target detection based on region proposal and CNN classifier

2021 ◽  
Author(s):  
Mingming Fan ◽  
Shaoqing Tian ◽  
Kai Liu ◽  
Jiaxin Zhao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Rate ◽  
Corner Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Potential Target ◽  
Infrared Small Target

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

Sign in / Sign up

Export Citation Format

Share Document