地理空间信息2025，Vol.23Issue(6)：37-42,6.DOI:10.3969/j.issn.1672-4623.2025.06.008
基于自监督学习的多尺度差分高光谱异常检测
Multi-scale Differential Hyperspectral Anomaly Detection Based on Self-supervised Learning
周晓忠 1刘军廷 1周海涛1
作者信息
- 1. 宁波冶金勘察设计研究股份有限公司,浙江 宁波 315194
- 折叠
摘要
Abstract
Hyperspectral anomaly detection analyzes the spectral features of both background and anomalous targets in an unsupervised manner to explore their distribution.Due to the complex distribution of hyperspectral backgrounds,training samples may contain anomalies that weaken the deep network model's generalization ability.Directly predicting abnormal targets is challenging for the network model,which reduces its practical application.In this paper,we proposed a multi-scale differential anomaly detection method based on self-supervised to improve the mod-el's generalization ability and enable it to predict anomalous targets directly.We designed a significant category search strategy based on K-means,and combined the entropy information measure and inter-class distance measure to optimize the marking of pseudo-anomalies and background samples.We iterated and updated the samples in a self-supervised learning manner.Additionally,we constructed a multi-scale differ-ential network structure based on center differential convolution,and achieved the end-to-end detection of anomalous targets by multi-scale fea-ture fusion and probability prediction.Experimental results of four hyperspectral data show that this algorithm has better detection performance while suppressing background interference effectively.关键词
高光谱异常检测/K-means聚类/中心差分卷积/多尺度Key words
hyperspectral anomaly detection/K-means clustering/center differential convolution/multi-scale分类
测绘与仪器引用本文复制引用
周晓忠,刘军廷,周海涛..基于自监督学习的多尺度差分高光谱异常检测[J].地理空间信息,2025,23(6):37-42,6.
