Automatic target recognition method for low-resolution ground surveillance radar based on 1D-CNN

2021 ◽  
Author(s):  
Renhong Xie ◽  
Bohao Dong ◽  
Peng Li ◽  
Yibin Rui ◽  
Xing Wang ◽  
...  
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Low Resolution ◽  
Recognition Method

scholarly journals Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs

2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
David Vint ◽  
Matthew Anderson ◽  
Yuhao Yang ◽  
Christos Ilioudis ◽  
Gaetano Di Caterina ◽  
...  
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Sar Images ◽  
Adversarial Networks ◽  
Technological Advances ◽  
Dataset Size ◽  
Resolution Imaging ◽  
High Level

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

scholarly journals Automatic target recognition method for multitemporal remote sensing image

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 170-181
Author(s):  
Chang Shu ◽  
Lihui Sun
Keyword(s):  
Remote Sensing ◽  
Image Recognition ◽  
Target Recognition ◽  
Automatic Segmentation ◽  
Image Data ◽  
Remote Sensing Image ◽  
Automatic Target Recognition ◽  
Image Feature ◽  
Recognition Method ◽  
Multitemporal Remote Sensing

AbstractThe traditional target recognition method for the remote sensing image is difficult to accurately identify the specified targets from the massive remote sensing image data. Based on the theory of multitemporal recognition, an automatic target recognition method for the remote sensing image is proposed in this article. The proposed recognition method includes four modules: automatic segmentation of multitemporal remote sensing image, automatic target extraction of multitemporal remote sensing image, automatic processing of multitemporal remote sensing image, and automatic recognition of multitemporal remote sensing image. The automatic segmentation of the image target is introduced. The effectiveness of the segmentation technology is verified through the kernel function bandwidth algorithm. Linear feature extraction is used to extract the segmented image. The image extraction processing is described, which includes image profile analysis, image preprocessing, image feature analysis, the region of interest localization, image enhancement processing, recognition processing, and result output. According to the theory of pattern recognition, three different feature recognition images are given, which are partial separable recognition, weakly separable recognition, and fully separable recognition, and then, a new image recognition method is designed. To verify the practical application effect of the recognition method, the proposed method is compared with the traditional recognition method. Experimental results show that the proposed method can accurately identify the specified objects from the massive remote sensing image data and has a high potential for development. This article has an important guiding significance for image recognition.

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.

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