多模态信息融合

Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery

Jamy Lafenetre, Ngoc Long Nguyen, Gabriele Facciolo, Thomas Eboli

专题命中 遥感融合与全色锐化 ：multi-exposure(title,abstract)；分类 cs.CV、eess.IV

Comments 9 pages

Deep dual stream residual network with contextual attention for pansharpening of remote sensing images

Syeda Roshana Ali, Anis Ur Rahman, Muhammad Shahzad

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening

Huanyu Zhou, Qingjie Liu, Dawei Weng, Yunhong Wang

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；分类 cs.CV、eess.IV

Comments 14 pages, 8 figures, and 7 tables. Accepted by TGRS

Proximal PanNet: A Model-Based Deep Network for Pansharpening

Xiangyong Cao, Yang Chen, Wenfei Cao

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Comments 9 pages, 6 figures

PanFormer: a Transformer Based Model for Pan-sharpening

Huanyu Zhou, Qingjie Liu, Yunhong Wang

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；分类 cs.CV、eess.IV

Comments Accepted by ICME 2022

Pansharpening by convolutional neural networks in the full resolution framework

Matteo Ciotola, Sergio Vitale, Antonio Mazza, Giovanni Poggi, Giuseppe Scarpa

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Journal ref IEEE Transactions on Geoscience and Remote Sensing

HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening

Wele Gedara Chaminda Bandara, Vishal M. Patel

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Comments Accepted at CVPR'22. Project page: https://www.wgcban.com/research#h.ar24vwqlm021 Code available at: https://github.com/wgcban/HyperTransformer

FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter

Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Kai Sun, Lu Huang, Junmin Liu, Chunxia Zhang

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Comments Accepted by ICME 2021 (Oral)

LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes

Jiahui Ni, Zhimin Shao, Zhongzhou Zhang, Mingzheng Hou, Jiliu Zhou, Leyuan Fang, Yi Zhang

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

On the validation of pansharpening methods

Gintautas Palubinskas

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Comments 18 pages, 5 figures, 6 tables. arXiv admin note: substantial text overlap with arXiv:2103.03062

MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening

Yuan Yuan, Yi Sun, Yuanlin Zhang

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；分类 cs.CV、eess.IV

Comments 37 pages, 14 figures

Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction

Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

PGMAN: An Unsupervised Generative Multi-adversarial Network for Pan-sharpening

Huanyu Zhou, Qingjie Liu, Yunhong Wang

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；分类 cs.CV、eess.IV

Comments Code is available <https://github.com/zhysora/PGMAN>

Pan-sharpening via High-pass Modification Convolutional Neural Network

Jiaming Wang, Zhenfeng Shao, Xiao Huang, Tao Lu, Ruiqian Zhang, Jiayi Ma

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；分类 cs.CV、eess.IV

Comments 5 pages, 5 figures, accepted by the 28th IEEE International Conference on Image Processing (ICIP 2021)

Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability

Ricardo Augusto Borsoi, Clémence Prévost, Konstantin Usevich, David Brie, José Carlos Moreira Bermudez, Cédric Richard

专题命中 遥感融合与全色锐化 ：image fusion(title,abstract)；分类 eess.IV、eess.SP

Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label

Wu Wang, Yue Huang, Xinhao Ding

专题命中 遥感融合与全色锐化 ：image fusion(title,abstract)；分类 cs.CV、eess.IV

Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information

Shuang Xu, Jiangshe Zhang, Kai Sun, Zixiang Zhao, Lu Huang, Junmin Liu, Chunxia Zhang

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Comments Accepted by ICME2021

Deep Gradient Projection Networks for Pan-sharpening

Shuang Xu, Jiangshe Zhang, Zixiang Zhao, Kai Sun, Junmin Liu, Chunxia Zhang

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；分类 cs.CV、eess.IV

Comments Accepted by CVPR2021

Learning deep multiresolution representations for pansharpening

Hannan Adeel, Syed Sohaib Ali, Muhammad Mohsin Riaz, Syed Abdul Mannan Kirmani, Muhammad Imran Qureshi, Junaid Imtiaz

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Unsupervised Pansharpening Based on Self-Attention Mechanism

Ying Qu, Razieh Kaviani Baghbaderani, Hairong Qi, Chiman Kwan

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Comments Accepted by TGRS

An Inter- and Intra-Band Loss for Pansharpening Convolutional Neural Networks

Jiajun Cai, Bo Huang

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV、eess.IV

Comments 4 pages, 2 figures

Multispectral Pan-sharpening via Dual-Channel Convolutional Network with Convolutional LSTM Based Hierarchical Spatial-Spectral Feature Fusion

Dong Wang, Yunpeng Bai, Ying Li

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；分类 eess.IV、cs.MM

S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks

Jae-Seok Choi, Yongwoo Kim, Munchurl Kim

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；分类 cs.CV、eess.IV

Comments Accepted for publication in IEEE Geoscience and Remote Sensing Letters

Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources

S. Salcedo-Sanz, P. Ghamisi, M. Piles, M. Werner, L. Cuadra, A. Moreno-Martínez, E. Izquierdo-Verdiguier, J. Muñoz-Marí, Amirhosein Mosavi, G. Camps-Valls

专题命中 遥感融合与全色锐化 ：information fusion(title,abstract)；分类 cs.CV

Journal ref Information Fusion, Volume 63, November 2020, Pages 256-272

G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening

G-ZAP：一种通用的零样本框架用于任意尺度的全色融合

Zhiqi Yang, Shan Yin, Jingze Liang, Liang-Jian Deng

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV

AI总结 本文提出G-ZAP框架，通过多尺度半监督训练方案实现跨分辨率、跨场景和跨传感器的泛化能力，在多个真实世界数据集上取得SOTA结果，支持权重重用以提高实际部署效率。

GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening

GSPan：一种用于任意尺度全色锐化的连续高斯基元表示

Fangyi Li, Xiaoyuan Yang, Yixiao Li, Zongyang Sui, Kangqing Shen, Gemine Vivone

机构 * Beihang University（北京航空航天大学） ； Tsinghua University（清华大学） ； National Research Council - Institute of Methodologies for Environmental Analysis, CNR-IMAA（意大利国家研究委员会 - 环境分析方法研究所） 纠错

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV

AI总结 提出GSPan框架，将2D高斯溅射引入全色锐化，通过连续可学习的2D高斯基元表示残差细节，实现任意尺度融合，无需重新训练。

DMAConv: Dual Mask-Adaptive Convolution for Remote Sensing Pansharpening

DMAConv: 用于遥感全色锐化的双掩膜自适应卷积

Xianghong Xiao, Zeyu Xia, Zhou Fei, Jinliang Xiao, Haorui Chen, Liangjian Deng

机构 * University of Electronic Science and Technology of China（电子科技大学） ； Tongji University（同济大学） 纠错

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV

AI总结 提出双掩膜自适应卷积（DMAConv），通过软硬掩膜动态分配计算资源，以轻量级双分支结构高效处理遥感图像的区域异质性，实现SOTA性能且计算成本最低。

UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations

UrbanFusion: 用于鲁棒空间表示对比学习的随机多模态融合

Dominik J. Mühlematter, Lin Che, Ye Hong, Martin Raubal, Nina Wiedemann

机构 * ETH Zurich（苏黎世联邦理工学院） 纠错

专题命中 遥感融合与全色锐化 ：multimodal fusion(title,abstract)；分类 cs.CV

AI总结 提出UrbanFusion模型，通过随机多模态融合（SMF）和Transformer模块整合街景、遥感、地图和POI数据，在56个城市41项任务中优于现有GeoAI模型。

Journal ref International Conference on Machine Learning (ICML), 2026

Fast Kernel-Space Diffusion for Remote Sensing Pansharpening

快速核空间扩散用于遥感全色锐化

Hancong Jin, Zihan Cao, Liang-jian Deng, Jingjing Li

机构 * University of Electronic Science and Technology of China（电子科技大学） 纠错

专题命中 遥感融合与全色锐化 ：pansharpening(title,abstract)；分类 cs.CV

AI总结 本文提出KSDiff框架，通过整合低秩核心张量生成器和统一因子生成器，利用结构感知的多头注意力机制生成增强全局上下文的卷积核，以提升全色锐化质量并加速推理，实验表明其在性能和效率上均优于现有方法。

Comments CVPR 2026 Findings

CC-Pan: Channel-wise Compression based Diffusion for Efficient Pan-Sharpening

CC-Pan: 基于通道压缩的扩散模型用于高效全色锐化

Junjie Li, Congyang Ou, Haokui Zhang, Guoting Wei, Shengqin Jiang, Ying Li

机构 * School of Cyberspace Security, Northwestern Polytechnical University（网络安全学院，西北工业大学） ； Nanjing University of Science and Technology（南京理工大学） ； School of Computer Science, Nanjing University of Information Science and Technology（计算机科学学院，南京信息工程大学） 纠错

专题命中 遥感融合与全色锐化 ：pan-sharpening(title,abstract)；分类 cs.CV

AI总结 本文提出CC-Pan，一种跨传感器潜在扩散框架，通过带级单通道变分自编码器编码高分辨率多谱段图像，结合轻量级区域基于跨带注意力模块，提升全色锐化精度与效率。