衛星影像判讀在許多方面具有重要應用。在部分應用時，為精確識別和分析事件，除依賴分析師的專業經驗與對事件背景的了解之外，關鍵在於能否迅速獲取高解析度的衛星影像，並清楚呈現目標細節。然而，偵照過程中受限於經費或遇到較大傾角或惡劣天氣條件，影像的空間解析度可能不如預期，導致物體細節模糊不清。但分析師仍需要高解析度影像來監控如非法走私船等特定目標，從而精準評估局勢。為解決此問題，本研究旨在採用Real-ESRGAN和Efficient Super-Resolution Transformer (ESRT) 兩種超解析度模型提升影像的空間解析度，使影像在視覺上更加銳利，並根據實驗結果評估這兩種方法的優劣。實驗結果顯示，經由Real-ESRGAN處理的高解析度和超高解析度影像在視覺效果上更為銳利，但部分細節無法完全重建，部分特徵可能會失真或紋理無法完整保留；相比之下，ESRT模型產生的超解析影像在邊緣銳利度上雖不及Real-ESRGAN，但在影像紋理細節的重建上表現較佳。
近年來，隨著衛星星群的快速發展，衛星影像數量大幅增加。然而，分析師在判讀影像時必須逐一檢視影像上每處細節，工作過程耗時費力。因此，本研究的另一個目標是運用物件偵測技術，快速識別影像中的關鍵目標，幫助分析師更快獲取重要資訊，提升整體工作效率。然而，由於高解析度影像的成本高昂，部分預算有限的使用者無法輕易取得，僅能購買較低解析度的影像。但較低解析度影像的細節不夠清晰，可能導致物件偵測的準確度下降，難以達到輔助分析師快速分析影像的效果。為解決此問題，本研究嘗試先利用超解析方法增強原始影像的空間特徵，並將增強後的影像輸入物件偵測模型進行測試。實驗結果顯示，相比與原始解析度影像，超解析影像應用於物件偵測的準確率顯著提高，另強化後的影像空間特徵能幫助分析師更準確地判釋目標，從而進行更全面的分析。

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.

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