<p>Multi-image Super-Resolution (MISR) reconstructs high-resolution images from multiple satellite-acquired low-resolution images, emerging as a key technique in remote sensing. However, image sequences collected by satellites usually have diverse views and extended time spans, making the integration of multiple low-resolution images into a single high-resolution image with intricate details a challenging problem. In this paper, we propose AttMISR, an attention-based multi-image super-resolution network for remote sensing. AttMISR is composed of three key modules: a feature extraction module utilizing residual dynamic convolution blocks, a hybrid non-local feature fusion module, and a multi-attention-based image reconstruction module. The feature extraction module integrates the residual structure with dynamic convolution to efficiently capture complex features and textures in remote sensing images, while the hybrid non-local feature fusion module optimizes feature aggregation across multiple remote sensing images by calculating both cross-correlation and non-cross-correlation features. Moreover, a coordinate-window attention mechanism is proposed to construct the multi-attention-based image reconstruction module, enabling more precise reconstruction. Comprehensive experiments conducted on PROBA-V Kelvin dataset demonstrate the superiority of the proposed method.</p>