关键词: 图像分割, 遥感图像, 无监督域适应, 全局和局部特征, 像素上下文关系

Abstract: Remote sensing image data have a large volume and a wide range of land cover categories. There is a significant disparity between local and global features, and noticeable differences in the intra-domain features. This makes it challenging for traditional transfer learning to effectively improve the model’s generalization performance. In light of this, based on the traditional unsupervised domain adaptation model that aligns features between domains, a prototype-combined two-stage unsupervised domain adaptation segmentation model for remote sensing images is proposed. Firstly, category features are represented using prototypes. The prototype acquisition module is introduced to obtain and update prototypes. By applying prototypes through the prototype imposition module in combination with self-attention, global category features are imposed on the locally cropped image features. This enables the segmentation network to consider both local and global category information, thus better extracting invariant features from both domains. Secondly, the target domain images are divided into easy and hard segments using pseudo-labels. Through adversarial and self-training methods, the intra-domain feature differences in the target domain are reduced, facilitating better extraction of intra-domain invariant features from easy and hard target domain images. Lastly, the segmentation prediction maps, which contain known pixel categories, are used to compute the contextual relationships between each pixel and its neighboring pixels. A pixel context relationship graph is generated to determine the domain from which the pixel context relationship in the output-level discriminative network originates. This compels the segmentation network to better extract domain-invariant contextual relationships and alleviate the problem of spectral confusion. Experimental results on two datasets demonstrate that the proposed model effectively mitigates the challenges posed by large disparities between local and global features, significant intra-domain feature differences, and the issue of spectral confusion, leading to a decline in model transfer performance. Compared with advanced domain adaptation segmentation methods, the proposed model exhibits superior performance.

Key words: image segmentation, remote sensing images, unsupervised domain adaptation, global and local features, pixel contextual relationship