A COMPLEX-VALUED CONVOLUTIONAL NEURAL NETWORK WITH DIFFERENT ACTIVATION FUNCTIONS IN POLARIMETRIC SAR IMAGE CLASSIFICATION

被引：3

作者：

Zhang, Yun ^{[1
]}

Hua, Qinglong ^{[1
]}

Xu, Dan ^{[1
]}

Li, Hongbo ^{[1
]}

Mu, HuiLin ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Elect Informat Engn, Harbin, Peoples R China

来源：

2019 INTERNATIONAL RADAR CONFERENCE (RADAR2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

Complex-valued convolutional neural network (CV-CNN); optimization algorithm; activation function; synthetic aperture radar (SAR); terrain classification; deep learning;

D O I：

10.1109/RADAR41533.2019.171298

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

It is well known that the activation function and the gradient descent optimization algorithm have a great influence on convolutional neural network (CNN). Based on the Adam optimization algorithm, this paper proposes a CvAdam optimization algorithm suitable for complex-valued convolutional neural network (CV-CNN), and then in the typical polarization SAR image classification task, Adam and CvAdam were compared using four different activation functions of sigmoid, tanh, Leakey-ReLU and ELU. Experiments on the benchmark dataset of Oberpfaffenhofen show that CvAdam performs better than Adam in both convergence speed and accuracy, no matter which activation function is used.

引用

页码：749 / 752

页数：4

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