现代电子技术2024，Vol.47Issue(19)：94-99,6.DOI:10.16652/j.issn.1004-373x.2024.19.015
基于改进反馈卷积自编码器的高光谱图像降维
Hyperspectral image dimensionality reduction based on improved feedback convolutional auto-encoder
摘要
Abstract
Hyperspectral images(HSIs)are characterized by high spectral resolution,abundant spectral features,imagery and spectral integration,and have been widely utilized in various fields,such as land utilization classification,urban planning and management,and forest resource investigation.However,the presence of a large amount of redundant information among different spectral channels in HSIs results in the high complexity of HSI dimensionality reduction algorithms and decreases its performance.In view of the above,an HSI dimensionality reduction method based on improved feedback convolutional auto-encoder is proposed in combination with the existing mainstream deep learning technology.To facilitate the information flows,a residual connection is introduced into the original encoder model,which promotes the gradient information propagation.To better capture the key features of the HSI data,a branching structure is incorporated into the original decoder model.The maximum pooling is replaced with the average pooling,and the mean absolute error(MAE)is used to replace the mean square error(MSE)loss function,so as to further optimize the feature extraction ability of the model and improve the performance of HSI dimensionality reduction.The experimental results show that the proposed model outperforms the latest methods in terms of HSI dimensionality reduction on the Indian Pines dataset,so it provides a new idea for HSI dimensionality reduction.关键词
高光谱图像/高光谱图像降维/反馈卷积/自编码器/深度学习/分支结构Key words
HSI/HSI dimensionality reduction/feedback convolution/auto-encoder/deep learning/branching structure分类
信息技术与安全科学引用本文复制引用
刘芳华,宋文杰..基于改进反馈卷积自编码器的高光谱图像降维[J].现代电子技术,2024,47(19):94-99,6.基金项目
河南省科技攻关项目(242102210013) （242102210013）
河南省高等学校青年骨干教师培养计划项目(2023GGJS090) （2023GGJS090）
国家自然科学基金项目(61502435) （61502435）
