Target recognition method based on single-pixel imaging system and deep learning in the noisy environment

2020 ◽  
Vol 49 (6) ◽  
pp. 20200010
Author(s):  
石峰 Feng Shi ◽  
陆同希 Tongxi Lu ◽  
杨书宁 Shuning Yang ◽  
苗壮 Zhuang Miao ◽  
杨晔 Ye Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Target Recognition ◽  
Noisy Environment ◽  
Recognition Method ◽  
Single Pixel

Target recognition method based on single-pixel imaging system and deep learning in the noisy environment

2020 ◽  
Vol 49 (6) ◽  
pp. 20200010
Author(s):  
石峰 Feng Shi ◽  
陆同希 Tongxi Lu ◽  
杨书宁 Shuning Yang ◽  
苗壮 Zhuang Miao ◽  
杨晔 Ye Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Target Recognition ◽  
Noisy Environment ◽  
Recognition Method ◽  
Single Pixel

scholarly journals A Deep Learning-Based Satellite Target Recognition Method Using Radar Data

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2008
Author(s):  
Lu ◽  
Zhang ◽  
Xu ◽  
Lin ◽  
Huo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Target Recognition ◽  
Recognition Performance ◽  
Recognition Task ◽  
Performance Testing ◽  
Radar Data ◽  
Data Partition ◽  
Distance Metric ◽  
Recognition Method

A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition.

scholarly journals Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4333
Author(s):  
Pengfei Zhao ◽  
Lijia Huang ◽  
Yu Xin ◽  
Jiayi Guo ◽  
Zongxu Pan
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Recognition Method ◽  
Training Samples ◽  
Model Training ◽  
Sar Target Recognition

At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems.

The Cat's Eye Effect Target Recognition Method Based on Visual Attention

2019 ◽  
Vol 28 (5) ◽  
pp. 1080-1086 ◽  
Author(s):  
Xingbin Wang ◽  
Jun Zhang ◽  
Shuaihui Wang
Keyword(s):  
Visual Attention ◽  
Target Recognition ◽  
Recognition Method
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