In recent years， the application scenarios of satellite remote sensing images have become increasingly diverse. However， due to limited collection equipment and cost constraints， the images obtained by satellite sensors usually do not have sufficient resolution and are not uniformly distributed， which is difficult to distinguish some clustered objects. Low-resolution remote sensing images are not suitable for semantic understanding tasks such as object detection and recognition to accurately locate and classify all objects. Compared to obtaining complete high-resolution remote sensing images at once， existing remote sensing images usually have relatively clear high resolution in local areas and sufficient detailed information for distinguishing object groups. Traditional remote sensing image super-resolution reconstruction methods mainly focus on the global features of remote sensing images， expanding resolution and pixels based on global features of images， while ignoring the details of remote sensing images. To address this problem， this paper proposes a method that introduces detailed information about local clustered object areas in local images in the reconstruction of complete remote sensing images. Specifically， the proposed method uses a multi-level neural network to extract image features of different scales and then uses residual learning to merge and reconstruct these features. In the experiments of this paper， the proposed method achieved better visual effects and numerical results compared to several existing remote sensing image super-resolution reconstruction methods. This indicates that the proposed method can effectively utilize the pixel information of local images， significantly improve the details of global remote sensing images and optimize the recognition capability of the group objects area， and enhance the quality and availability of remote sensing images in a low-cost way.