###

计算机系统应用英文版:2021,30(8):243-248

←前一篇 | 后一篇→

码上扫一扫！

下载全文

基于ResNet双注意力机制的遥感图像场景分类

乔星星^1,2,3,4, 施文灶^1,2,3,4, 刘芫汐^1,2,3,4, 林耀辉^1,2,3,4, 何代毅^1,2,3,4, 王磊^1,2,3,4, 温鹏宇^1,2,3,4, 孙雯婷^1,2,3,4

(1.福建师范大学光电与信息工程学院, 福州 350007;2.福建师范大学福建省光电传感应用工程技术研究中心, 福州 350007;3.福建师范大学医学光电科学与技术教育部重点实验室, 福州 350007;4.福建师范大学福建省光子技术重点实验室, 福州 350007)

Remote Sensing Image Scene Classification Based on ResNet and Dual Attention Mechanism

QIAO Xing-Xing^1,2,3,4, SHI Wen-Zao^1,2,3,4, LIU Yuan-Xi^1,2,3,4, LIN Yao-Hui^1,2,3,4, HE Dai-Yi^1,2,3,4, WANG Lei^1,2,3,4, WEN Peng-Yu^1,2,3,4, SUN Wen-Ting^1,2,3,4

(1.College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China;2.Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China;3.Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China;4.Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China)

摘要

图/表

参考文献

相似文献

本文已被：浏览 1003次下载 2224次
Received:November 27, 2020 Revised:December 28, 2020

中文摘要: 针对基于传统机器学习遥感图像场景分类无法快速有效提取图像特征造成分类结果不准确的问题, 提出一种基于注意力残差网络的遥感图像场景分类的方法, 以残差网络为基准模型, 在通道和空间两个维度上建立注意力模块, 实验过程中对参数进行合理有效的设置, 调整网络层数优化模型, 达到对UC Merced Land-Use数据集的有效分类. 实验结果表明, 与基于卷积神经网络结构的遥感图像场景分类方法相比, 该方法达到了98.1%的准确率.

中文关键词: 遥感图像场景分类卷积神经网络残差网络注意力模块

Abstract:To deal with the inaccurate classification caused by a failure of quick and effective extraction of image features in the remote sensing image scene classification based on existing machine learning methods, we propose a remote sensing image scene classification method based on residual attention network. With the residual network as the benchmark model, attention modules are created in the dimensions of channel and space. For effective classification of the UC Merced Land-Use dataset, parameters are set reasonably and the model that optimizes the number of network layers is fine-tuned. The results show that the accuracy of our method reaches 98.1% compared with that based on the convolution neural network.

keywords: remote sensing image scene classification Convolutional Neural Network (CNN) Residue Network (ResNet) attention module

文章编号： 中图分类号： 文献标志码：

基金项目:国家自然科学基金青年基金（41701491）；福建省自然科学基金面上项目（2017J01464，2018J01619）

Author Name	Affiliation	E-mail
QIAO Xing-Xing	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
SHI Wen-Zao	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China	swz@fjnu.edu.cn
LIU Yuan-Xi	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
LIN Yao-Hui	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
HE Dai-Yi	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
WANG Lei	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
WEN Peng-Yu	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
SUN Wen-Ting	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China

Author Name	Affiliation	E-mail
QIAO Xing-Xing	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
SHI Wen-Zao	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China	swz@fjnu.edu.cn
LIU Yuan-Xi	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
LIN Yao-Hui	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
HE Dai-Yi	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
WANG Lei	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
WEN Peng-Yu	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China
SUN Wen-Ting	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China

引用文本：
乔星星,施文灶,刘芫汐,林耀辉,何代毅,王磊,温鹏宇,孙雯婷.基于ResNet双注意力机制的遥感图像场景分类.计算机系统应用,2021,30(8):243-248
QIAO Xing-Xing,SHI Wen-Zao,LIU Yuan-Xi,LIN Yao-Hui,HE Dai-Yi,WANG Lei,WEN Peng-Yu,SUN Wen-Ting.Remote Sensing Image Scene Classification Based on ResNet and Dual Attention Mechanism.COMPUTER SYSTEMS APPLICATIONS,2021,30(8):243-248