OPTIMIZING CLOUD BASED IMAGE STORAGE, DISSEMINATION AND PROCESSING THROUGH USE OF MRF AND LERC

• Main Authors: P. Becker, L. Plesea, T. Maurer
• Format: Article
• Language: English
• Published: Copernicus Publications 2016-06-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B4/201/2016/isprs-archives-XLI-B4-201-2016.pdf
• ISSN: 1682-1750; 2194-9034

The volume and numbers of geospatial images being collected continue to increase exponentially with the ever increasing number of airborne and satellite imaging platforms, and the increasing rate of data collection. As a result, the cost of fast storage required to provide access to the imagery is a major cost factor in enterprise image management solutions to handle, process and disseminate the imagery and information extracted from the imagery. Cloud based object storage offers to provide significantly lower cost and elastic storage for this imagery, but also adds some disadvantages in terms of greater latency for data access and lack of traditional file access. Although traditional file formats geoTIF, JPEG2000 and NITF can be downloaded from such object storage, their structure and available compression are not optimum and access performance is curtailed. <br><br> This paper provides details on a solution by utilizing a new open image formats for storage and access to geospatial imagery optimized for cloud storage and processing. MRF (Meta Raster Format) is optimized for large collections of scenes such as those acquired from optical sensors. The format enables optimized data access from cloud storage, along with the use of new compression options which cannot easily be added to existing formats. The paper also provides an overview of LERC a new image compression that can be used with MRF that provides very good lossless and controlled lossy compression.