Efficient Remote Sensing Segmentation With Generative Adversarial Transformer

EMANTIC segmentation, as a significant task in image processing, has found application in various practical the field of computer vision, has quickly become a research scenarios such as autonomous driving, precision agriculture, hotspot due to its capability to learn explicit global and longrange and urban analysis [4]. Over the past decade, inspired by semantic features [2], [5]. Nevertheless, previous studies the success of deep learning in high-level visual tasks, a have overlooked the non-local textures with low similarity, considerable amount of work has been devoted to using deep which might offer richer detail information than highly similar convolutional neural networks (DCNNs) for semantic segmentation features [13]. Additionally, although global features can be of remote sensing images [1], [8], [15]. The inherent captured, Transformer also result in higher computational characteristics of geographical objects in remote sensing images, complexity because each position's feature needs to be computed including their multi-scale nature, random appearances, and interacted with other positions.