Parallel Classification and Detection System for High-Resolution Remote Sensing Images

In view of the characteristics of high score remote sensing data containing rich semantic information, combined with different processing requirements for high-score remote sensing images, this paper designs and implements a parallel classification and detection system based on high-score remote sensing image. With the development of remote sensing technology and artificial intelligence technology, it is of great significance to process high-score remote sensing image data based on deep learning convolution neural network. The system integrates sea land segmentation model and target detection model, which can realize different types of data processing requirements. It can realize the high-efficiency processing of large-scale images by means of parallel processing technology, and realize the detection effect of the specified targets by visual processing technology. It is applicable to the processing tasks of high-resolution remote sensing images of all types, and has a high level of intelligent and parallel processing.