# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 1 Introduction  
The semantic segmentation of images using computers has a wide range of application scenarios in remote sensing, medicine [1], agriculture [2, 3], and other fields. The semantic segmentation of remote-sensing images is an important part of processing and analyzingremote-sensing data and is one of the most widely used areas of remote-sensing applications [4; 5; 6]. The accurate and rapid acquisition of remote-sensing image classification is important for urban management, resource investigation, environmental monitoring, natural disaster assessment, and military reconnaissance. This is because it provides managers with a source of information for more-robust decision-making [7; 8]. For example, [PERSON] et al., used remote sensing to detect nitrogen, phosphorus, and potassium elements in widely grown crops in Africa for the purpose of protecting the environment while increasing food production [9]. [PERSON] et al., analyzed the remotely sensed images of Miami Park cliffs at the edge of East Lake Michigan to study the factors of park cliff deformation and prevent disasters from cliff landslides [10]. However, the rich and complex features in remote-sensing images have been a challenge for segmentation. Early semantic segmentation methods rely mainly on manual visual interpretation to classify remote-sensing images, which not only is time-consuming and laborious but also relies heavily on experience for classification accuracy. As the resolution of remote-sensing images continues to develop, image-element-based and object-oriented semantic segmentation methods are becoming widely used [11]. The image-element-based method makes full use of mainly the spectral reflection information of remotely sensed features for classification, but it lacks a consideration of the relationship between adjacent image elements [12]. Object-oriented methods are used for classification in the framework of object-based image analysis [13], and the problems with such methods are that they are prone to noise, and the classification scale of different features is difficult to determine [14]. Owing to the improvement of computer technology, machine learning has also been applied to the research of remote-sensing image segmentation [15]. Different from the previous methods, machine learning extracts a large number of remote-sensing image features by different classifiers, further reducing the problem of using a human interference. The main methods include decision trees, support vector machines (SVMs), and random forests (RF) [16]. For example, [PERSON] et al., used a large number of different advanced support vector machines to fully learn previously unlabelable data with a view to providing guidance for the ensuing research on remote-sensing applications [17]. [PERSON] et al., used two effective methods, random forest and rotation forest, for classification in order to fully learn the texture features of polarimetric synthetic aperture radar remote-sensing images [18]. Although machine learning has significantly improved in efficiency and accuracy, this improvement applies only to its ability to extract the shallow-feature information of remote-sensing images. The efficiency and the accuracy of classification are still low when faced with complex remote-sensing images [19; 20]. Deep-learning methods have powerful and fast modeling capabilities that can improve segmentation by using the spectral information and texture features of remote-sensing images [21]. In remote-sensing image semantic segmentation, using convolutional neural networks (CNNs) is a popular deep-learning method that has significantly better image semantic segmentation capability than previous methods and has been widely used in both academic and industrial fields [22]. Thanks to its excellent ability to express high-level semantic features, CNN and its derivatives have shown potential in many image semantic segmentation tasks.