期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B3
页码：672-677
出版社：Copernicus Publications
摘要：In the recent years, airborne digital imaging sensors have gained acceptance in the photogrammetric workflow. However, the processing and management of data acquired by these sensors requires an enormous computational effort, which is often too high for single processor architectures. This demand for processing power stems from the large amount of data being generated and the high rate of automation possible in ground processing. Distributed computing, the method of dividing large processing problems into smaller tasks that can run on individual systems, has emerged as a key enabling technology for digital photogrammetric workflows. Using networks of workstations in distributed computing to solve large problems has become popular owing to the proliferation of inexpensive, powerful workstations. Clusters offer a cost-effective alternative to batch processing and an easy entry into parallel computing. The main advantage is the potential for future performance enhancement that results from the high rate of advances seen in computer and network hardware, scalability, fault tolerance and rapid development of applications. This paper summarizes a range of distributed computation technologies surveyed, design criteria used for choosing a solution and the results obtained in the ground processing workflow of the Leica Airborne Digital Sensor, especially in rectification and automated point matching. We conclude by presenting the results from real applications indicating timesaving and benefits of the distributed computing model in a photogrammetric production department
关键词：Distributed; Automation; P rocessing; Digital; Sensor; Rectification; Matching; Performance
