航空学报2026，Vol.47Issue(10)：93-108,16.DOI:10.7527/S1000-6893.2025.32432
自回归与反馈驱动的自适应矩形卷积全色锐化网络
AFAR-Net:Autoregressive and feedback-driven adaptive rectangular convolution network for pansharpening
摘要
Abstract
To enhance spectral fidelity and spatial detail restoration in remote sensing pansharpening tasks,this pa-per proposes a deep pansharpening network based on an encoder-decoder architecture,named the Autoregressive and Feedback-Driven Adaptive Rectangular Convolution Network for Pansharpening(AFAR-Net).The proposed net-work employs an autoregressive mechanism,where the output of the previous unit is used to optimize the current one,enabling progressive multi-level image reconstruction.Meanwhile,a feedback-driven fusion module is designed to effi-ciently integrate deep features across units,thereby enhancing spectral consistency.On the other hand,an adaptive convolutional residual block is introduced to flexibly adjust kernel sizes and shapes,strengthening the network's ability to model and restore complex spatial structures.Finally,a lightweight fusion head is utilized to aggregate multi-level predictions,improving the stability of reconstruction.Experimental results on multiple remote sensing datasets demon-strate that the proposed network outperforms existing mainstream approaches in terms of spatial distortion,Spectral Angle Mapper(SAM),and the Quality with No Reference(QNR)index,showing strong generalization ability and ap-plication potential.关键词
遥感图像/全色锐化/自回归/自适应卷积/深度学习Key words
remote sensing image/pansharpening/autoregressive/adaptive convolution/deep learning分类
航空航天引用本文复制引用
段韶华,张淳杰,刘传凯,郑晓龙,张济韬..自回归与反馈驱动的自适应矩形卷积全色锐化网络[J].航空学报,2026,47(10):93-108,16.基金项目
国家自然科学基金(62476021,72225011,72434005,62373034) （62476021,72225011,72434005,62373034）
多模态人工智能系统全国重点实验室开放课题(MAIS2024106) （MAIS2024106）
中央高校基本科研业务费专项资金(2025JBZX062) National Natural Science Foundation of China(62476021,72225011,72434005,62373034) （2025JBZX062）
Open Project of State Key Laboratory of Multimodal Artificial Intelligence Systems(MAIS2024106) （MAIS2024106）
Fundamental Research Funds for the Central Universities(2025JBZX062) （2025JBZX062）
