吉林大学学报（信息科学版）2026，Vol.44Issue(2)：341-355,15.
基于深度变化特征的能源基础设施遥感图像检索方法
Retrieval Methods of Remote Sensing Image for Energy Infrastructure Based on Depth Variation Characteristics
摘要
Abstract
To address the limitations of traditional image retrieval methods that are predominantly constrained to single-phase data and lack comprehensive research on time-series remote sensing images,a novel change information retrieval model,SCanNet-Retrieval(Semantic Change Network and Retrieval)is proposed,which aims to enhance the performance of change information retrieval for dual-phase images.The architecture of SCanNet-Retrieval comprises two primary modules,the feature extraction module and the similarity measurement module.The feature extraction module integrates an encoder-decoder structure with the SCanFormer module and incorporates a category change matrix to effectively capture spatiotemporal semantic change features.In the similarity measurement module,the Jaccard similarity coefficient is employed to assess retrieval performance.Three other similarity measurement methods,Euclidean distance,Manhattan distance,and Hamming distance are compared with validate the effectiveness of the proposed model.To address the scarcity of publicly available two-phase datasets in the domain of energy infrastructure,the EICIRD(Energy Infrastructure Change Information Retrieval Dataset)is constructed.Experimental results indicate that SCanNet-Retrieval achieves an average retrieval accuracy exceeding 93％across all change categories,significantly outperforming other methods.This underscores its potential for efficient and accurate retrieval of energy infrastructure change information from large-scale time-series image data.This method offers critical support for the intelligent monitoring of energy infrastructure and the green transformation of the energy industry.关键词
能源基础设施/深度学习/图像检索/变化检测Key words
energy infrastructure/deep learning/image retrieval/change detection分类
信息技术与安全科学引用本文复制引用
袁影,赵满,许红飞,王梅,王志宝..基于深度变化特征的能源基础设施遥感图像检索方法[J].吉林大学学报（信息科学版）,2026,44(2):341-355,15.基金项目
国家重点研发计划基金资助项目(2022YFC330160204) （2022YFC330160204）
黑龙江省高等教育教学改革基金资助项目(SJGY20200125) （SJGY20200125）
东北石油大学环渤海能源研究所2020年托海专项基金资助项目(HBHZX202002) （HBHZX202002）
