Super-resolution reconstruction technology plays an important role in the intelligent processing of satellite remote sensing images. Existing deep learning methods for super-resolution remote sensing image reconstruction can only handle super-resolution tasks with a single scale factor, lacking generalization at the multiscale level and failing to meet the requirements of real super-resolution remote sensing image reconstruction for continuous zooming at multiple magnification levels.To address the problem of arbitrary-scale super-resolution reconstruction and enhance the quality of reconstructed real remote sensing images, this paper proposes a super-resolution reconstruction method called the Meta-RDCAN, which utilizes meta-learning and residual dense channel attention network.The proposed method employs a meta-upscale module that incorporates three functions: weight prediction, location projection, and feature mapping. The module adaptively adjusts the internal parameters of a model according to different scale factors for arbitrary-scale super-resolution reconstruction. From the perspective of extracting detailed information of local land objects in a remote sensing image, a dense residual network with an attention mechanism is used as a feature extractor, enabling the reconstructed results to possess clear and distinguishable details.Extensive experiments are conducted on various datasets, including DIV2K, AID, UCMerced, WIDS, Set5, and real remote sensing image from Macao Science Popularization Satellite. The influence of variations in spatial resolution on the super-resolution reconstruction results is analyzed, and the effectiveness of the training scheme, which involves pretraining on a general dataset followed by fine-tuning on a remote sensing dataset, is validated using the loss curve.The test results with different scale factors demonstrate that the proposed model is suitable for arbitrary-scale super-resolution reconstruction tasks in remote sensing images, having scale factors of up to 4.0. Comparative experimental results show that the improved model with added channel attention achieves enhanced performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM), compared with the baseline model. On real remote sensing data, the reconstruction results of the proposed model achieve a PSNR of over 40 dB and an SSIM of over 0.95. The comparison based on the no-reference quality indicator NIQE confirms that the perceived quality of the super-resolution reconstruction results of the improved model surpasses that of the baseline model.The proposed method for arbitrary-scale super-resolution reconstruction is effective for remote sensing images by utilizing meta-learning and dense residual channel attention. The main contributions of this paper include two aspects. First, for the arbitrary-scale super-resolution reconstruction of remote sensing image, the meta-learning approach is employed to adaptively adjust the internal parameters of a model. This approach enables continuous integer- and non-integer-scale super-resolution reconstruction of a single remote sensing image with a single model. Second, to address issues, such as missing details and unclear edges of geographic features, in reconstruction results, a channel-attention mechanism is applied to enhance the dense residual network and improve the quality of super-resolution reconstruction results.

super-resolution reconstruction;remote sensing image;arbitrary-scale;meta-learning;residual dense network;channel attention mechanism