高分辨率光学卫星影像高精度在轨实时云检测的流式计算

本文重点阐述基于机器视觉的智能摄影测量的效率基础问题之二：高精度影像在轨实时云检测方法。随着技术发展，数据获取能力不断提升，待处理的数据量呈爆炸式增长；同时，对处理精度需求的提升，导致所需计算量的不断增长，二者凸显了智能摄影测量面临的效率问题。对光学卫星影像而言，高达50%的平均云覆盖率严重制约了高效精准在轨智能摄影测量的实现。针对于此，本文结合机器视觉中“自底向上”的图像理解控制策略，提出一种可供借鉴的基于流式计算的高分辨率光学卫星影像高精度在轨实时云检测方法，采用适合在轨搭载的嵌入式GPU实现实时流式计算，为后续的智能摄影测量处理提供输入。本文方法采用不依赖外存的快速处理机制，对持续流入的数据实时分块，通过负载均衡机制将数据块依次分发至各个单元并行处理，从而实现“流入、处理、流出”的实时处理。利用高分二号数据对本文方法进行试验验证，结果表明本文方法在显著提高云覆盖区域检测精度的同时，综合加速比达14，可满足在轨实时处理需求。

Stream-computing Based High Accuracy On-board Real-time Cloud Detection for High Resolution Optical Satellite Imagery

This paper focuses on the time efficiency for machine vision and intelligent photogrammetry,especially high accuracy on-board real-time cloud detection method.With the development of technology,the data acquisition ability is growing continuously and the volume of raw data is increasing explosively.Meanwhile,because of the higher requirement of data accuracy,the computation load is also become heavier.This situation makes time efficiency extremely important.Moreover,the cloud cover rate of optical satellite imagery is up to approximately 50%,which is seriously restricting the applications of on-board intelligent photogrammetry services.To meet the on-board cloud detection requirements and offer valid input data to subsequent processing,this paper presents a stream-computing based high accuracy on-board real-time cloud detection solution which follows the “bottom-up” understanding strategy of machine vision and uses multiple embedded GPU with significant potential to be applied on-board.Without external memory,the data parallel pipeline system based on multiple processing modules of this solution could afford the “stream-in,processing,stream-out” real-time stream computing.In experiments,images of GF-2 satellite are used to validate the accuracy and performance of this approach,and the experimental results show that this solution could not only bring up cloud detection accuracy,but also match the on-board real-time processing requirements.