Journal ArticleDOI
Support vector machines for SAR automatic target recognition
Qun Zhao,Jose C. Principe +1 more
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TLDR
Experimental results showed that SVMs outperform conventional classifiers in target classification because SVMs with the Gaussian kernels are able to form a local "bounded" decision region around each class that presents better rejection to confusers.Abstract:
Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc, are receiving more and more attention in the literature. A real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) is presented and the result is compared with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local "bounded" decision region around each class that presents better rejection to confusers.read more
Citations
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Target Classification Using the Deep Convolutional Networks for SAR Images
TL;DR: A new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used, which can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.
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Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine
Youngwook Kim,Hao Ling +1 more
TL;DR: The feasibility of classifying different human activities based on micro-Doppler signatures is investigated and the potentials of classify human activities over extended time duration, through wall, and at oblique angles with respect to the radar are investigated and discussed.
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Convolutional Neural Network With Data Augmentation for SAR Target Recognition
TL;DR: The capability of a deep convolutional neural network (CNN) combined with three types of data augmentation operations in SAR target recognition is investigated, showing that it is a practical approach for target recognition in challenging conditions of target translation, random speckle noise, and missing pose.
References
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