Name
Abstract | ||
|---|---|---|
Complex-valued convolutional neural network (CV-CNN) has been presented in recent years. In this letter, CV full convolutional neural network (CV-FCNN) is proposed for synthetic aperture radar (SAR) target classification, which contains only convolution layers in the hidden layer. The purpose of replacing both the pooling and fully connected layers in CV-CNN with the convolution layers is to avoid complex pooling operation and prevent overfitting, respectively. Considering the label of target is always real-valued, the magnitude of the complex vector obtained from the last convolution layer is calculated before softmax classification in the output layer. Moreover, the back-propagation formula for each layer of CV-FCNN is presented in detail. Furthermore, the complex
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 1$ </tex-math></inline-formula>
convolution layer is added into CV-FCNN to learn the cross-channel information of feature maps. The experimental results show that the average accuracy can be improved using CV-FCNN, and it is further improved using CV-FCNN with the
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 1$ </tex-math></inline-formula>
convolution layer. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LGRS.2019.2953892 | IEEE Geoscience and Remote Sensing Letters |
Keywords | DocType | Volume |
Convolution,Synthetic aperture radar,Training,Convolutional neural networks,Kernel,Apertures | Journal | 17 |
Issue | ISSN | Citations |
10 | 1545-598X | 2 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling-Juan Yu | 1 | 2 | 1.37 |
| Yuehong Hu | 2 | 2 | 0.36 |
| Xiao-Chun Xie | 3 | 2 | 1.03 |
| Yun Lin | 4 | 2 | 0.36 |
| HONG Wen | 5 | 7 | 7.73 |