[1]王茂发,冯十辰,黄鸿亮,等.基于短距空间光谱并行双向RNN的高光谱农业图像分类[J].南京师范大学学报(工程技术版),2022,22(04):001-8.[doi:10.3969/j.issn.1672-1292.2022.04.001]
　Wang Maofa,Feng Shichen,Huang Hongliang,et al.Shorten Special-spectral Parallel Bidirectional RNN for Hyperspectral Agricultural Image Classification[J].Journal of Nanjing Normal University(Engineering and Technology),2022,22(04):001-8.[doi:10.3969/j.issn.1672-1292.2022.04.001]
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基于短距空间光谱并行双向RNN的高光谱农业图像分类
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
22卷
期数:
2022年04期
页码:
001-8
栏目:
计算机科学与技术
出版日期:
2022-12-15

文章信息/Info

Title:
Shorten Special-spectral Parallel Bidirectional RNN for Hyperspectral Agricultural Image Classification
文章编号:
1672-1292(2022)04-0001-08
作者:
王茂发1; 冯十辰1; 黄鸿亮2; 龚启舟3; 万 泉1; 徐 智1
(1.桂林电子科技大学广西可信软件重点实验室,广西 桂林 541004)
(2.澳门大学科技学院数学系,中国 澳口 999078)
(3.北京信息科技大学理学院,北京 100192)
Author(s):
Wang Maofa1; Feng Shichen1; Huang Hongliang2; Gong Qizhou3; Wan Quan1; Xu Zhi1
(1.Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin 541004,China)
(2.Department of Mathematics,School of Science and Technology,University of Macau,Macau 999078,China)
(3.School of Applied Science,Beijing Information Science and Technology University,Beijing 100192,China)
关键词:
深度学习; 门控循环单元; 长短时记忆网络; 循环神经网络; 高光谱图像分类
Keywords:
deep learning; gate recurrent unit(GRU); long short term mermory network(LSTM); recurrent neural networks(RNN); hyperspectral image classification
分类号:
TP391.4
DOI:
10.3969/j.issn.1672-1292.2022.04.001
文献标志码:
A
摘要:
提出一种新的短距空间并行双向RNN算法(shorten spatial-spectral parallel bidirectional RNN,St-SS-pBRNN)用于高光谱农业图像分类,通过组合多个卷积层实现了频谱和空间特征的同时利用,提升了图像的分类效果. 采用并行门控循环单元(gate recurrent unit,GRU)和双向RNN的组合架构,缩短了RNN的序列长度,大幅减少了模型的计算量. 在农业高光谱图像分类对比实验中,算法性能稳定,准确率比经典的短距空间并行GRU算法(shorten spatial-spectral parallel GRU,St-SS-pGRU)最优效果提升大于2%,相关模型有望在国内大范围的农业用地分类中得以推广应用.
Abstract:
A method is proposed for HSI classification using shorten spatial-spectral parallel bidirectional RNN(St-SS-pBRNN). By combining multi-convolutional layers,it not only uses the frequency information of spectrum but also considers the spatial characteristics in the images,thereby obtaining better performance and effects. Meanwhile,a combined architecture of parallel gate recurrent unit(GRU)and bidirectional RNN is adopted,which shortens the sequence length of RNN and greatly reduces the amount of calculation of the model. In the comparative experiments of agricultural hyperspectral image classification,the performance of the algorithm is stable and accurate,and its accuracy is more than 2% higher than the optimal effect of the classical shorten spatial-spectral parallel GRU(St-SS-pGRU). It is expected to be popularized and applied in a wide range of agricultural land classification in China.

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备注/Memo

备注/Memo:
收稿日期:2022-03-16.
基金项目:国家自然科学基金资助项目(42164002,41504037)、广西科技计划项目(桂科AD20325004、桂科AD19110022).
通讯作者:王茂发,博士,副教授,研究方向:数据挖掘. E-mail:wangmaofa2008@126.com
更新日期/Last Update: 2022-12-15