红外技术2025，Vol.47Issue(6)：712-721,10.
一种引入多尺度卷积滤波器的高光谱特征提取方法
A Feature Extraction Method of Hyperspectral Image with Multi-Scale Convolutional Filters
摘要
Abstract
To address the problem of gradient vanishing in recurrent neural networks and the limited receptive field of traditional convolutional neural networks,this paper proposes a spectral-spatial feature extraction method that incorporates multi-scale convolutional filters.The method consists of two main components:spectral feature extraction and spatial feature extraction.In the spectral feature extraction stage,a bidirectional long short-term memory(Bi-LSTM)network is combined with a band-grouping strategy.This approach mitigates the gradient vanishing issue caused by excessive network depth.In the spatial feature extraction stage,multi-scale convolutional filters are introduced based on a convolutional neural network(CNN),allowing the model to capture both fine details and global structural information.Additionally,shallow features are fused with deep features to further enhance classification performance.Experimental results on two datasets demonstrate that the proposed method effectively improves classification accuracy.关键词
高光谱图像/特征提取/深度学习/双向长短时记忆网络/多尺度卷积滤波器Key words
hyperspectral images/feature extraction/deep learning/Bi-LSTM/multiscale convolutional filter分类
计算机与自动化引用本文复制引用
黄飞庆,郭宝峰,尤靖云,吴治龙,王奕炜,王庆林..一种引入多尺度卷积滤波器的高光谱特征提取方法[J].红外技术,2025,47(6):712-721,10.基金项目
国家自然科学基金(61375011). （61375011）
