## 1 Introduction  
The processing and analysis of remote-sensing images have gained increasing importance in light of the rapid advancements in imaging technologies. Consequently, the automated extraction of essential information from remote-sensing images has emerged as a critical research domain within the field of remote-sensing image processing. In particular, semantic segmentation has proven to be one of the most significant advances in remote-sensing image technology [1; 2; 3], which is applied to a variety of fields, including environmental monitoring, land resource utilization, and urban planning. Compared with natural images, remote-sensing images exhibit the properties of high resolution, complex content, and large differences in object scale. Additionally, because of the intricate nature of the image content, segmentation accuracy is higher in real-world applications. Semantic segmentation of remote-sensing images presents a significant challenge due to the complexity of imaging, diverse object categories, and substantial scale variations among objects [4; 5]. A remote-sensing image contains rich information about ground objects; how to accurately segment the real area is still a long-term challenge [6]. The traditional remote-sensing image segmentation methods usually use threshold segmentation [7], edge detection [8], pixel clustering [9], etc. The robustness of classic segmentation algorithms is poor, and deep semantic information is hard to extract. Deep learning has been developing quickly lately,and, as a result, it is becoming a necessary tool for computer vision [5]. Convolutional neural networks (CNNs) have strong learning abilities and can autonomously acquire rich spectral and spatial characteristics from images. Therefore, several scholars have successfully employed it in tasks related to remote-sensing image segmentation [10; 11]. Different from traditional CNN, a full convolutional network (FCN) [12] can achieve pixel-level image classification. It replaces the fully connected layer of traditional CNNs and employs upsampling to restore the image to its original size for pixel-level segmentation. Semantic segmentation methods based on FCN are constantly developed and improved for remote-sensing images. For instance, [PERSON] et al. [13] introduced FCN to semantic segmentation to improve the segmentation effect and achieve pixel-to-pixel segmentation. Building upon FCN, [PERSON] et al. [14] established the U-Net model, utilizing a symmetric coding structure combined with feature fusion to enhance segmentation accuracy. U-Net has earned widespread adoption in image segmentation applications [15]. Another notable approach, SegNet [16] was proposed with a coding-decoding structure based on FCN and used atrous convolution and a conditional random field to improve segmentation outcomes. The utilization of classical semantic segmentation networks for remote-sensing images often leads to non-ideal segmentation accuracy, primarily due to the substantial variations in target scale and the intricate nature of edge details. Therefore, semantic segmentation often utilizes two kinds of strategies. One strategy is multiscale feature fusion. For instance, [PERSON] et al. [17] presented a convolutional network with a multiscale and skip connection structure to extract both shallow and deep features at varying scales, established upon the U-Net architecture. Similarly, [PERSON] et al. [18] presented a multiscale fusion model to obtain multimodal features using the nonlocal mechanism and dilated convolutional layers for remote-sensing images. [PERSON] et al. [19] proposed a new cross-scale semantic feature network by using the multiscale convolution module to obtain multiscale context from different receptive fields. [PERSON] et al. [20] developed a multi-resolution attention model based on multiscale channel and spatial attention for exciting important features. [PERSON] et al.