Aiming at the low efficiency of traditional methods for the management and classification of massive remote sensing image data, a mass remote sensing image classification method based on distributed storage is proposed. The aim is to obtain near real-time image classification in mobile devices or internet applications. In this paper, we designed two levels of an image processing structure. A distributed file system is taken as the underlying storage architecture to efficiently manage and query massive remote sensing images. The upper layer uses a GPU server to train the remote sensing image classification model to improve the classification accuracy. To improve the classification accuracy, we add two parameters to adjust the data of the current layer in U-Net. The experimental results show that the proposed method based on distributed storage has a high degree of scalability, and it has a short processing time while maintaining a high classification accuracy for remote sensing images.

针对传统方法对海量遥感影像数据的管理和分类效率低的问题，提出了一种基于分布式存储的海量遥感影像分类方法。目的是在移动设备或互联网应用中获得近乎实时的图像分类。在本文中，我们设计了两个级别的图像处理结构。分布式文件系统被用作底层存储体系结构，以有效地管理和查询海量遥感影像。上层使用GPU服务器训练遥感图像分类模型，以提高分类精度。为了提高分类的准确性，我们添加了两个参数来调整U-Net中当前层的数据。