红外技术2025，Vol.47Issue(4)：429-436,8.
不均衡少标签样本下基于语义自动编码网络的高光谱图像分类
Hyperspectral Image Classification Based on Improved Semantic AutoEncoder Network in Unbalanced Small-Sized Labeled Samples
摘要
Abstract
To improve the classification performance of hyperspectral images with unbalanced,few-labeled samples,an improved semantic autoencoder network is proposed in this paper.This network first introduces hyperspectral category-label information into the semantic autoencoder model,establishing the association between known and unknown categories by mapping the original data and label information of different datasets to the same feature space.It then maps the training dataset features to the unified embedding space to learn the correspondence between the visual features and the semantic features of the category labels.Finally,an objective function based on a graph regularization term is constructed to preserve the feature manifold structure in the dataset,and the global problem is decomposed into several smaller,more manageable local subproblems using the alternating direction multiplier method to obtain the global optimal solution.Three hyperspectral datasets with different spectral dimensions,numbers of spectral bands,and land cover types were selected to ensure the diversity of the experimental data.The results showed that the proposed method achieved better classification accuracy with a small number of labeled samples compared with other state-of-the-art methods,making it suitable for the engineering classification of unbalanced hyperspectral image data.关键词
高光谱图像/地物分类/深度学习/语义自动编码网络/语义关联/特征映射Key words
hyperspectral image/terrain classification/deep learning/semantic auto-encoder network/semantic association/feature mapping分类
电子信息工程引用本文复制引用
孙宝刚,何国斌..不均衡少标签样本下基于语义自动编码网络的高光谱图像分类[J].红外技术,2025,47(4):429-436,8.基金项目
国家自然科学基金项目(31071319),2022年重庆教委研究项目(22SKGH493). （31071319）
