The applied research in remote sensing images has been pushed by convolutional neural network (CNN). Because of the fixed size of the perceptual field, CNN is unable to model global semantic relevance. Modeling global semantic information is possible with the self-attentive Transformer-based model. However, the method of patch computation used by Transformer for self-attentive computation ignores the spatial information inside each patch. To address these issues, we offer the STransFuse model as a new semantic segmentation method for remote sensing images. It is a model that combines the benefits of Transformer with CNN to improve the segmentation quality of various remote sensing images. We employ a staged model to extract coarse-grained and fine-grained feature representations at various semantic scales, unlike earlier techniques based on Transformer model fusion. In order to take full advantage of the features acquired at different stages, we designed an Adaptive Fusion Module (AFM). This module adaptively fuses the semantic information between features at different scales employing a selfattentive mechanism. The OA of our proposed model on the Vaihingen dataset is 1.36% higher than the baseline, and 1.27% improvement in OA over baseline on the Potsdam dataset. When compared to other advanced models, the STransFuse model performs admirably.

Remote sensing images contain various land surface scenes and different scales of ground objects, which greatly increases the difficulty of super-resolution tasks. The existing deep learning-based methods cannot solve this problem well. To achieve high-quality super-resolution of remote sensing images, a residual aggregation and split attentional fusion network (RASAF) is proposed in this article. It is mainly divided into the following three parts. First, a split attentional fusion block is proposed. It uses a basic split-fusion mechanism to achieve cross-channel feature group interaction, allowing the method to adapt to various land surface scene reconstructions. Second, to fully exploit multi-scale image information, a hierarchical loss function is used. Third, residual learning is used to reduce the difficulty of training in super-resolution tasks. However, the respective residual branch features are used quite locally and fail to represent the real value. A residual aggregation mechanism is used to aggregate the local residual branch features to generate higher quality local residual branch features. The comparison of RASAF with some classical super-resolution methods using two widely used remote sensing datasets showed that the RASAF achieved better performance. And it achieves a good balance between performance and model parameter number. Meanwhile, the RASAF's ability to support multi-label remote sensing image classification tasks demonstrates its usefulness.

The digital economy now plays a pivotal role in reshaping the global economic structure and optimizing the allocation of resources. With the popularization of digital technology in rural areas, the impact of the digital economy on rural development is also increasing. In order to explore the impact of the digital economy on rural revitalization in Xinjiang of China, this study constructed an indicator system based on the data from 14 prefectures or cities (of the same administrative level as the prefectures) in Xinjiang from 2013 to 2019. The entropy weight method and coupling coordination degree (CCD) model were used to analyze the digital economy level (DEL) and rural revitalization level (RRL) in Xinjiang, and the relationship between the digital economy and rural revitalization was emphasized. Finally, the obstacle degree model was used to reveal the factors that hinder the coupled and coordinated development between the digital economy and rural revitalization. The research shows that: (1) Xinjiang’s DEL generally increased steadily, and digital economy development in 14 prefectures or cities had strong spatial heterogeneity. At the same time, Xinjiang’s RRL showed similar characteristics. (2) The CCD between the two systems was increasing, and the regional distribution features of high-level CCD were in northern Xinjiang and low-level CCD in southern Xinjiang. The coupling coordination was in its infancy, but the interaction between the two systems was increasing, and the development prospect was broad. (3) Overall, the main obstacle affecting the CCD between the digital economy and rural revitalization was the digital infrastructure among four factors, including digital investment, thriving businesses, social etiquette and civility, and effective governance. The degree of this obstacle varied in different phases of coupling coordination development.

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