摘要
为提升星载高光谱遥感影像的空间分辨率,克服单一数据源在时空和光谱信息表达上的局限性,本文提出了一种基于深度时空谱特征的高光谱数据融合方法。该方法融合了高光谱影像的丰富光谱信息与多光谱影像的高空间细节,实现了星载高光谱数据空间分辨率的提升。根据生成对抗网络思想设计了高光谱数据融合网络,优化了特征融合策略,有效增强了模型对不同分辨率影像的处理能力。试验结果表明,相比传统方法,本文所提的数据融合方法在保持空间结构与光谱一致性方面均具有更优的表现,多项定量评价指标验证了该方法的有效性与稳健性。本文方法为高光谱遥感影像的增强与应用提供了技术支撑,具有重要的理论意义和实践价值。
To address the limited spatial resolution of hyperspectral satellite imagery,this study proposes a data fusion method driven by deep spatio-spectral-temporal features.The method integrates the rich spectral information of hyperspectral images with the fine spatial details of multi-spectral data,aiming to generate fused imagery with both high spectral and spatial resolution.The fusion network is built upon a generative adversarial network(GAN)architecture,with an optimized feature fusion strategy that significantly enhances the network s capability in handling multi-resolution data.Experiments conducted on a comprehensive dataset,comprising both spaceborne and airborne hyperspectral imagery,demonstrate that the proposed method notably improves image quality,outperforming conventional approaches in terms of spatial detail preservation and spectral consistency.Quantitative evaluation using multiple metrics further confirms the robustness and effectiveness of the method.This study provides essential technical support for the enhancement and application of hyperspectral remote sensing imagery,offering important theoretical and practical value.
作者
潘琛
汪晓楚
王志威
PAN Chen;WANG Xiaochu;WANG Zhiwei(Shanghai Municipal Institute of Surveying and Mapping,Shanghai 200063,China;Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities,Shanghai 200063,China;School of Geographic Science,East China Normal University,Shanghai 200241,China)
出处
《测绘通报》
北大核心
2025年第8期118-122,共5页
Bulletin of Surveying and Mapping
基金
长三角自然科学基金(2024CSJZN01304)。
关键词
高光谱影像
多光谱影像
数据级融合
生成对抗网络
高光谱数据融合
hyperspectral image
multi-spectral image
data-level fusion
generative adversarial network
hyperspectral data fusion
