标题: Deep Metric Learning Based on Brownian Covariance Representation for Few-Shot Hyperspectral Image Classification

作者: Dong, YN (Dong, Yanni); Zhu, B (Zhu, Bei); Yang, XC (Yang, Xiaochen); Ma, X (Ma, Xin)

来源出版物: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 卷: 63 文献号: 5507813 DOI: 10.1109/TGRS.2025.3549633 Published Date: 2025

摘要: Currently, few-shot learning (FSL) is widely used in image classification hyperspectral image classification (HSIC), owing to its exceptional proficiency in achieving good performance with few training samples. Although the FSL has made good progress, there are still some problems to be solved. On the one hand, existing methods rely on linear distance to learn metrics, which cannot capture the subtle similarities and differences between scarce prior samples. On the other hand, many current methods directly superimpose the features of spatial and spectral information, without deeply fusing the internal relationship between these two kinds of information. To address the aforementioned issues, a deep metric learning method based on Brownian distance covariance (DML-BDC) is proposed for few-shot HSIC. A dual-channel Brownian distance covariance feature extraction network is designed, which uses the Brownian covariance representation to model and fuse the spatial and spectral information and uses two different feature extractors to achieve the effect of information complementarity. Then, a metric loss based on Gaussian kernel distance is proposed to learn the complex nonlinear structure and subtle similarities and differences between support samples. Experiments on three benchmark datasets show that DML-BDC has advantages over the existing mainstream methods in terms of classification accuracy, generalization, and model complexity.

作者关键词: Measurement; Feature extraction; Training; Hyperspectral imaging; Data mining; Image classification; Adaptation models; Kernel; Classification algorithms; Overfitting; Covariance representation (CR); deep metric learning (DML); few-shot; hyperspectral image classification (HSIC)

地址: [Dong, Yanni] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Zhu, Bei] China Univ Geosci, Sch Geophys & Geomat, Wuhan 430074, Peoples R China.

[Yang, Xiaochen] Univ Glasgow, Sch Math & Stat, Glasgow G12 8QQ, Scotland.

[Ma, Xin] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China.

通讯作者地址: Ma, X (通讯作者)，Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China.

电子邮件地址: dongyanni@whu.edu.cn; beizhu@cug.edu.cn; Xiaochen.Yang@glasgow.ac.uk; maxin@whu.edu.cn

影响因子：7.5