Location Intelligence | Pitney Bowes
MRR - A New Raster File Format
Pitney Bowes has developed a new raster data format called Multi-Resolution Raster (MRR) as a key component of the new MapInfo Pro Advanced raster visualization and processing solution.
MRR is a completely new raster data format that reimagines how raster data is stored and what data can be stored as a raster. It is a unifying and enabling technology. It unifies the storage of multiple raster data types, including satellite imagery, gridded data (e.g. digital terrain models) and vector-based thematic data. MRR removes the barriers to working with large and complex raster files, and enables the highest quality visualization and processing of raster data at any scale and for rasters of any size.
Large Raster File Sizes
MRR is designed to store rasters of virtually unlimited size. It imposes no performance penalty on working with increasingly larger file sizes. A MRR of any size can be visualized instantly in MapInfo Pro Advanced at any scale.
MRR extends the concept of a raster from a simple two dimensional array of cells to an extensible, sparse matrix of tiles. This model is far better suited to modern surveying techniques (e.g. LIDAR) which may collect data over a highly irregular coverage polygon. It can significantly reduce storage requirements and also frees the MRR from the limitations of a fixed size. An MRR can be extended as new data is acquired.
In addition to the base resolution level, an MRR contains a pyramid of overview levels at reducing resolution. Applications can access data at a resolution suitable for their needs. For example, when rendering to the screen or hardcopy device, we access data from a level in the pyramid where the raster cell size is comparable to the size of a pixel.
An MRR does not have a fixed size and can be extended by adding tiles anywhere, at any time. This is generally done by adding an edit event. These temporal events are stored separately within the MRR file and are complimentary to pre-existing data in the raster. This history of data acquisition is available to users who can extract data at a specific time or over a period of time.
MRR unifies raster data types by allowing storage of any of the four fundamental ‘field’ types – Image, Image (Palette), Classified (thematic) and Continuous. An MRR can efficiently store imagery as either continuous color values or as color indices linked to a palette. It can store continuous data such as spectral imagery or gridded data like elevation. It is a particularly good vessel for hyper-spectral imagery which may contain hundreds of bands of spectral data. It can store classified data where each cell is linked to a flexible table of data. In all cases, MRR removes traditional barriers that limit the size and complexity of the raster database.
Efficient storage is a pre-requisite enabling technology for large raster files. MRR provides a variety of techniques that users can employ to minimize storage requirements. These include sparse storage, flexible data type support, data transformations, control over decimal precision, predictive encoding and a variety of industry standard compression techniques encompassing lossless and lossy encoders. By employing thoughtful design and appropriate compression technology, users can typically achieve size reductions of between 4x and 1000x over traditional rasters. MRR also allows advanced users to employ tile based decimation which will reduce the resolution of the data locally where appropriate and further reduce storage requirements.
MRR achieves this flexibility and efficiency through a specially developed compound file system which allows it to be stored on disk (or in the cloud) as a single file. An MRR is easy to share!
With the renaissance in new earth observation satellites and venture funded companies, decreasing cost of acquiring LiDAR data and the emergence of unmanned aerial vehicles, the availability in type and quantity of raster data will continue to grow. MRR provides an efficient file type to process and analyze raster data in recognition of this trend.
To learn more, meet with the PB Team at the USGIF GEOINT Symposium 2016 in Orlando, Florida, May 15-18.