## 1 Introduction
This complexity underscores the substantial intricacies involved in this area of research and development. When it comes to recognizing a single label for RSIR, several freely available benchmark datasets have been established [6]. However, it is worth noting that, within the domain of remote sensing, there is a scarcity of studies that have provided public datasets specifically tailored for image-segmentation-based aircraft detection. This scarcity of publicly available datasets has posed a limitation on the development of innovative solutions in this area. Consequently, if one intends to train a network using real data for aircraft detection, the available training dataset may be relatively small. In response to this challenge, and by leveraging the advancements in Convolutional Neural Networks (CNNs), researchers have introduced various CNN architectures, such as U-Net [17], which have demonstrated remarkable performance even with limited training data. In this study, we propose a pixel-wise image segmentation model that draws inspiration from the specialized architecture of CNNs. The primary framework for our model is based on U-Net, which was selected for its effectiveness in achieving the rapid and high-quality recognition of aircraft objects. To evaluate the performance of our aircraft recognition model, we employ the dense labeling remote sensing dataset (DLRSD) [4]. This dataset serves as a valuable resource for assessing the capabilities of the planned model in the context of aircraft recognition [18]. The contributions of this study can be summarized as follows:  
1. The developed architecture presents an end-to-end semantic segmentation model with robust expandability. This model can be effectively employed for the automatic recognition of aircraft, including those that are not present in the initial training dataset. Importantly, this recognition can be achieved without the need to retrain the model. 2. This study integrates several strategic approaches, including data preprocessing and data augmentation, with the model designed for aircraft semantic segmentation. These strategies collectively contribute to a substantial improvement in accuracy, enhancing the model's performance. 3. To the best of our knowledge, this study represents the first effort to explore the DLRSD for the development of a semantic segmentation model tailored for aircraft recognition by employing the U-Net architecture. This exploration expands the scope of available resources and insights in the field of aircraft recognition, making a notable contribution to the literature. The remainder of this article is divided into the following sections: To provide context and insights into the body of knowledge that is already available in the field of aircraft recognition utilizing U-Net and deep learning, Section 2 explores the background and relevant works in the literature. This study's materials and methods are described in depth in Section 3, including the research strategies, data pretreatment methods, and the use of U-Net and deep learning for aircraft recognition. In Section 4, the findings are discussed, the experimental data are presented and analyzed, and the performance and effectiveness of the suggested semantic segmentation model are evaluated. Section 5 discusses the results. Finally, Section 6 serves as the conclusion, summarizing the key findings and contributions and extending the discussion to potential future research directions within the scope of aircraft recognition using U-Net and deep learning.