Infrared Small Target Segmentation with Multiscale Feature Representation

2021 ◽  
pp. 103755
Author(s):  
Lian Huang ◽  
Shaosheng Dai ◽  
Tao Huang ◽  
Xiangkang Huang ◽  
Hailing Wang
Keyword(s):  
Feature Representation ◽  
Small Target ◽  
Target Segmentation ◽  
Infrared Small Target

scholarly journals Robust Principal Component Analysis-Based Infrared Small Target Detection

2019 ◽  
Vol 33 ◽  
pp. 9925-9926
Author(s):  
Qiwei Chen ◽  
Cheng Wu ◽  
Yiming Wang
Keyword(s):  
Iteration Process ◽  
Principal Component ◽  
Component Analysis ◽  
Low Rank ◽  
Small Target ◽  
Frame Sequence ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Infrared Small Target ◽  
Augmented Lagrange Multiplier Method

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.

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.

Infrared Small Target Detection via Nonconvex Tensor Fibered Rank Approximation

2021 ◽  
pp. 1-21
Author(s):  
Xuan Kong ◽  
Chunping Yang ◽  
Siying Cao ◽  
Chaohai Li ◽  
Zhenming Peng
Keyword(s):  
Target Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Rank Approximation ◽  
Infrared Small Target

Infrared small target detection algorithm based on feature salience

2011 ◽  
Vol 98 (1) ◽  
pp. 137-145 ◽  
Author(s):  
Zhen-Xue Chen ◽  
Cheng-Yun Liu ◽  
Fa-Liang Chang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
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
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