结合深度时空谱特征的高光谱数据融合方法

doi:10.13474/j.cnki.11-2246.2025.0819

测绘通报 ›› 2025, Vol. 0 ›› Issue (8): 118-122.doi: 10.13474/j.cnki.11-2246.2025.0819

结合深度时空谱特征的高光谱数据融合方法

潘琛^1,2, 汪晓楚¹, 王志威³

1. 上海市测绘院, 上海 200063;
2. 自然资源部超大城市自然资源时空大数据分析应用重点实验室, 上海 200063;
3. 华东师范大学地理科学学院, 上海 200241

收稿日期:2025-07-01 出版日期:2025-08-25 发布日期:2025-09-02
通讯作者: 汪晓楚。E-mail:xchwang@shsmi.cn E-mail:xchwang@shsmi.cn
作者简介:潘琛(1981—),女,博士,高级工程师,主要从事遥感影像处理与城市遥感应用等研究工作。E-mail:panpan_tj@126.com
基金资助:
长三角自然科学基金(2024CSJZN01304)

Hyperspectral and multi-spectral data fusion method combined with deep spatio-spectral-temporal features

PAN Chen^1,2, WANG Xiaochu¹, WANG Zhiwei³

1. Shanghai Municipal Institute of Surveying and Mapping, Shanghai 200063, China;
2. Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Shanghai 200063, China;
3. School of Geographic Science, East China Normal University, Shanghai 200241, China

Received:2025-07-01 Online:2025-08-25 Published:2025-09-02

摘要/Abstract

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。

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

Abstract: 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.

Key words: hyperspectral image, multi-spectral image, data-level fusion, generative adversarial network, hyperspectral data fusion

中图分类号:

P237

潘琛, 汪晓楚, 王志威. 结合深度时空谱特征的高光谱数据融合方法[J]. 测绘通报, 2025, 0(8): 118-122.

PAN Chen, WANG Xiaochu, WANG Zhiwei. Hyperspectral and multi-spectral data fusion method combined with deep spatio-spectral-temporal features[J]. Bulletin of Surveying and Mapping, 2025, 0(8): 118-122.

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结合深度时空谱特征的高光谱数据融合方法

Hyperspectral and multi-spectral data fusion method combined with deep spatio-spectral-temporal features

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