An extensible high resolution remote sensing image storage structure for cloud computing

An extensible high resolution remote sensing image storage structure for cloud computing

Shen Sheng Liu Jiezhangping cang Zhang Wu Yi Chen Xiaoping

The traditional remote sensing image processing method has not been able to effectively deal with the 3 "massive" problems of remote sensing image, that is, the mass of daily production, the single pixel mass and the observable object category and the data mass, which makes the utilization of multi-source remote sensing data extremely low. In order to solve the problem of high resolution remote sensing image storage, a high resolution remote sensing image storage organization structure which is suitable for cloud computing is presented, and the construction method based on MapReduce framework is introduced in detail. A method of constructing large files of small image sets on the Hadoop cluster based on mass high-resolution remote sensing images the comparison of reading efficiency between the experiment and the traditional same way it is proved that the storage organization structure has high expansibility, and the small image set method has the ability of efficient and high data reading and processing. A data source suitable for processing massive high-resolution remote sensing images

An extensible high resolution remote sensing image storage structure for cloud computing