| dc.description.abstract | Satellite image interpretation is crucial in assorted applications. To accurately identify and analyze targets or events, in addition to relying on analysts′ expertise and understanding of the background, a helpful approach to obtaining high-resolution satellite images that present target details quickly. However, due to budget and other operational factors, if large off-nadir angles or adverse weather conditions are encountered, the spatial resolution of the image may not be enough, making object details unclear. Despite this, analysts still require high-resolution images to monitor specific targets, such as illegal smuggling vessels, to assess the situation accurately. To address this issue, this study enhances image spatial resolution using two super-resolution (SR) models, Real-ESRGAN and Efficient Super-Resolution Transformer (ESRT), making the images visually sharper. The study evaluates the advantages and disadvantages of these two methods. Experimental results show that images processed with Real-ESRGAN become visually sharper, especially in high-resolution and very high-resolution images. However, some details may not be fully reconstructed, and certain features may be distorted or textures not well preserved. In contrast, the super-resolution images produced by the ESRT model are less sharp in terms of edge clarity but perform better in reconstructing image texture details.
In addition, with the rapid development of satellite constellations, the objective of satellite images has significantly increased. However, analysts must examine every detail of the images during interpretation, making the process time-consuming and labor-intensive. Therefore, another objective of this study is to apply object detection technology to quickly identify critical targets within the images, helping analysts to obtain important information more efficiently and improve overall task operation. However, due to the high cost of high-resolution images, some users with limited budgets can only afford lower-resolution images. The lack of precise details in these lower-resolution images may reduce object detection accuracy, making it challenging for analysts to quickly interpret the images. To address this issue, this study attempts first to enhance the spatial features of the original images using super-resolution methods, then analyze the enhanced images with object detection models. The experimental results indicate that compared to the original resolution images, object detection accuracy has been significantly improved when using super-resolution images. Additionally, the enhanced spatial features of the images help analysts interpret targets more accurately, enabling more comprehensive analysis. | en_US |