为提升遥感全色锐化任务中光谱信息的保真性和空间细节的恢复能力，本文提出了一种基于编码-解码架构的深度全色锐化网络，自回归与反馈驱动的自适应矩形卷积网络（Autoregressive and Feedback-driven Adaptive Rectangular convolution Network, AFAR-Net）。该网络使用自回归机制，通过前一单元的预测结果优化当前单元，实现多级图像重建，其中反馈驱动融合模块被用于跨单元深度特征的高效融合，可有效提升光谱信息的一致性。同时，自适应卷积残差块可灵活调整卷积核大小与形状，增强网络对复杂空间结构的建模能力和恢复效果。最终轻量级融合头融合多级预测结果以提升图像重建的稳定性。在多个遥感数据集上的实验结果表明，所提方法在空间结构保持度、光谱角误差（Spectral Angle Mapper, SAM）和无参考图像质量指数（Quality with No Reference, QNR）上均优于现有主流方法，展现出良好的泛化性和应用潜力。

To enhance spectral fidelity and spatial detail restoration in remote sensing pansharpening tasks, this paper 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 network employs an autoregressive mechanism, where the output of the previous unit is used to optimize the current one, enabling progressive multi-level image reconstruction. In addition, a feedback-driven fusion module is designed to efficiently integrate deep features across units, thereby enhancing spectral consistency. On the other hand, an adaptive rectangular 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 demonstrate that the proposed method outperforms existing mainstream approaches in terms of spatial distortion, spectral angle mapper (SAM), and the quality with no reference index (QNR), showing strong generalization ability and application potential.