Remote sensing image super-resolution (RSISR) plays a key role in recovering spatial details and improving image quality from satellite imagery. In recent years, transformer-based methods have shown excellent performance in RSISR tasks. Despite the higher computational efficiency of local self-attention calculations compared to global self-attention calculations, its limited receptive field restricts the model from effectively modeling the complex scale diversity and long-range dependencies of ground observation targets. Moreover, the intermediate features of existing methods contain blocking artifacts, leading to different degrees of feature edge distortion and texture detail loss. To address the above issues, this paper proposes the adaptive multi-scale feature aggregation transformer network (AMFAT), which improves the feature representation capability through dynamic weighting and cross-window interaction. Specifically, the adaptive context channel attention (ACCA) is designed to fuse multi-branch features using dynamic weights for object-guided context adaptation. In addition, the mixed-scale token attention (MSTA) is constructed to eliminate blocking artifacts through cross-window interaction. Meanwhile, simple gating units with spatial enhancement operations are introduced into the feed-forward network (FFN) to optimize local feature aggregation. We conducted extensive experiments on four publicly available remote sensing datasets, and the results show that, compared to other methods, AMFAT exhibits excellent performance and adaptability both in terms of quantitative metrics and visual quality. The model will be available at https://github.com/sq-3768/AMFAT.

Current remote sensing object detection frameworks often focus solely on the geometric relationship between true and predicted boxes, neglecting the intrinsic shapes of the boxes. In the field of remote sensing detection, there are numerous elongated bounding boxes. Variations in the shape and size of these boxes result in differences in their Intersection over Union (IoU) values, which is particularly noticeable when detecting small objects. Platforms with limited resources, such as satellites and unmanned drones, have strict requirements for detector storage space and computational complexity. This makes it challenging for existing methods to balance detection performance and computational demands. Therefore, this paper presents RS-YOLO, a lightweight framework that enhances You Only Look Once (YOLO) and is specifically designed for deployment on resource-limited platforms. RS-YOLO has developed a bounding box regression approach for remote sensing images, focusing on the shape and scale of the boundary boxes. Additionally, to improve the integration of multi-scale spatial features, RS-YOLO introduces a lightweight multi-scale hybrid attention module for cross-space fusion. The DOTA-v1.0 and HRSC2016 datasets were used to test our model, which was then compared to multiple state-of-the-art oriented object detection models. The results indicate that the detector introduced in this article achieves top performance while being lightweight and suitable for deployment on resource-limited platforms.