In the era of big data, the research on data-intensive computing is becoming more and more popular both at home and abroad. As a typical branch of big data, remote sensing data is characterized both by the variety of the data sources and the huge data quantity. One of the biggest challenges facing the remote sensing application is how to find out a data-intensive computing method which aims at the automation of the business processions of remote sensing images. In this paper, a new data-intensive computing method for the procession of remote sensing images is proposed. After a deeply study focusing on the automation of the business processions of remote sensing data, a new systematic architecture using workflow is introduced which can coordinate the work among different algorithm models. In addition, in the pre-processing of the remote sensing images, a new computing architecture with five different types of parallelism and a stage of acceleration is also adopted. The computing method proposed in this paper has been tested in many products in real production environment in order to testify its effectiveness. The results show a significant improvement on the efficiency of the pre-processing of remote sensing data in the condition of ensuring the processing precision.