无线电工程2025，Vol.55Issue(12)：2373-2384,12.DOI:10.3969/j.issn.1003-3106.2025.12.006
基于ARM架构与Docker的Swin-Transformer遥感影像云检测方法研究
Remote Sensing Image Cloud Detection with Swin-Transformer on ARM-based Docker
摘要
Abstract
For remote sensing image segmentation and classification applications on specific platforms,a Swin-Transformer-based remote sensing image cloud detection method is proposed,which is deployed using ARM architecture and Docker containerization.By constructing unsigned 16-bit image-label samples,the spectral details of objects are preserved without compression loss.Compared to traditional 8-bit natural images,this approach improves the separability and detection accuracy of high-brightness categories such as clouds and snow.Additionally,for ARM architecture hardware and operating systems,a cross-platform deployment solution based on Docker containerization technology is adopted to achieve consistent encapsulation and flexible migration of the algorithm environment.Data experiments show that using a Swin-Transformer model pre-trained on ImageNet-1k samples for small-block inference,combined with fine-tuning for model iteration and an active learning strategy for model iteration,the object classification accuracy in complex scenarios is improved.Meanwhile,the ARM-based Docker deployment solution maintains cross-platform compatibility,providing a feasible technical path for remote sensing intelligent interpretation in specific environments.关键词
ARM/Docker/Swin-Transformer/分割/云检测Key words
ARM/Docker/Swin-Transformer/segmentation/cloud detection分类
信息技术与安全科学引用本文复制引用
陆俊南,戴山,胡昌苗..基于ARM架构与Docker的Swin-Transformer遥感影像云检测方法研究[J].无线电工程,2025,55(12):2373-2384,12.基金项目
国家重点研发计划(2024YFD1500802)National Key R&D Program of China(2024YFD1500802) （2024YFD1500802）
