Infrared small target detection based on target-background separation via local MCA sparse representation

2017 ◽  
Author(s):  
Hao Fu ◽  
Yunli Long ◽  
Jungang Yang ◽  
Wei An
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

Robust infrared small target detection via non-negativity constraint-based sparse representation

2016 ◽  
Vol 55 (27) ◽  
pp. 7604 ◽  
Author(s):  
Minjie Wan ◽  
Guohua Gu ◽  
Weixian Qian ◽  
Kan Ren ◽  
Qian Chen
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

Infrared small target detection in heavy sky scene clutter based on sparse representation

2017 ◽  
Vol 85 ◽  
pp. 13-31 ◽  
Author(s):  
Depeng Liu ◽  
Zhengzhou Li ◽  
Bing Liu ◽  
Wenhao Chen ◽  
Tianmei Liu ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

scholarly journals Infrared small target detection based on image sparse representation

2012 ◽  
Vol 30 (2) ◽  
pp. 156-161 ◽  
Author(s):  
Jia-Jia ZHAO ◽  
Zheng-Yuan TANG ◽  
Jie YANG ◽  
Er-Qi LIU ◽  
Yue ZHOU
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

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.

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