## 1 Introduction  
Images acquired by aerial platforms and satellites for remote sensing purposes are described as depictions of the Earth's surface from a vantage point in space. The semantic labeling of these images is widely recognized as a critical challenge [1]. This task involves assigning a class label to each pixel within an image. Over the past ten years, advancements in technology, such as compact imaging sensors and unmanned aerial vehicles, have yielded significant enhancements in image quality while reducing operational and equipment costs [2]. These cost-effective platforms provide flexible access to both multi-spectral and high-resolution images, along with an accelerated data acquisition rate [3]. Consequently, a significant challenge has emerged concerning the efficient management of extensive data collections and the swift retrieval of specific data of interest. Content-Based Image Retrieval (CBIR) is recognized as a valuable approach for rapidly accessing desired images within large-scale datasets [4]. In recent years, substantial effort has been dedicated to tailoring CBIR techniques for remote sensing images, leading to the emergence of a vibrant and complex field known as Remote Sensing Image Retrieval (RSIR) [5]. Within this field, researchers have placedparticular emphasis on the advancement of feature extraction methods. This focus is critical because the effectiveness of image retrieval largely hinges on the quality and efficiency of the features used [6]. As a result, numerous methodologies have been developed for the application of semantic segmentation in the context of aerial and satellite images. These techniques represent innovative approaches to enhance the retrieval and analysis of remote sensing data, addressing the unique challenges and complexities of this domain. These techniques may be divided into three main categories. Methods using manual feature extraction methods, such as the Scale-Invariant Feature Transform (SIFT) method, fall under the first group [7], including Vector of Locally Aggregated Descriptors (VLAD) [8], improved Fisher kernel [9], bag of visual words [10], local binary patterns, and Gabor texture [11, 12]. With these techniques, features are manually extracted from remote sensing photos. The second kind uses common classifiers that need previous knowledge of the feature extraction procedure to categorize individual pixels, such as Conditional Random Fields (CRFs) or Random Forest. Deep learning methods, which have become popular in the semantic segmentation of satellite pictures, are what distinguish the final category. These models, which frequently use neural networks, are capable of automatically learning contextual characteristics from the input data, such as higher-level specifics and shape aspects [13, 14]. They have changed this area of research by enabling more automated and adaptable feature extraction, and they are known for their capacity to extract complex information [1]. In order to meet various criteria and circumstances for the semantic segmentation of satellite pictures, each of these categories provides unique benefits and trade-offs. The automatic detection of aircraft in high-resolution satellite images represents a significant and intricate challenge in the realm of target identification. This challenge holds critical importance across both military and civilian domains, with dynamic airfield surveillance being one of its prominent applications [15]. High-resolution satellite images offer a wealth of spatial information, textures, and colors due to their superior quality. However, automating the recognition of aircraft in such images proves to be a highly demanding task, primarily because of the complex and diverse structures depicted in satellite imagery. Furthermore, aircraft exhibit variations in terms of shape, color, and size. Even for a single type of aircraft, the intensity and texture of its appearance can differ significantly across various scenarios [16].