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Building Spatial Databases – Theory / Raster data structures

Learners guide


In this chapter raster data structures will be introduced, where raster data are stored in a regular grid.


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Raster data structures

Raster data structures are simpler than vector-based, since their common property is a table: each row and column contains information on a pixel, which can be intensity, height values, etc. Let us take a look at Figure 51, where a panchromatic (greyscaled) image has been described in the above mentioned table structure.

Figure 51. Panchromatic (greyscaled) image table representation.

There are many raster file formats present, but most of them are based on table structure, therefore we will not discuss these file formats in detail. Figure 51 describes a schematic representation of a raster image, because most of the software companies dealing with remote sensing are working on the same basis with their own modifications on the method of transforming data into their own data format. This knowledge will be gained when concrete usage of these software is required. Data table on Figure 51 contains only one intensity band, however in the case of RGB-based image, there will be three data tables (intensity matrixes) representing the image. In the case of 32-bit images, a fourth intensity matrix is also required to describe the transparency data.

In the case of multi-spectral remote sensed images along with RGB bands, each infrared band has their own intensity matrix. By taking into consideration the speed of development in hyperspectral remote sensing, an image may contain hundreds of intensity matrixes. It is quite a challenge to handle the large amount of data, not to mention the interpretation of each band's data, therefore we may state that remote sensing is an enormous research area waiting to be exploited.

Well-known raster data formats

Many raster-based data formats are used in every day applications as well as in remote sensing. Some of the formats are closely related to operating systems (e.g. bmp) or compressed images created by losing (jpg and some of the tiff formats) and lossless (tif, gif, png or jpg2000) algorithms, while others are not compressed. A few data formats will be described, since many books are discussing them thoroughly.

Tiff. Used for general purposes, especially applied in progressional image processing areas. Being platform independent, it is widely spread and used in remote sensing. The pixel-based data representation is capable of storing image parameters, text and numeric-based data along with pixel data. It supports both shallow and professional 24- or 32-bit colour models, while the CMYK colour models are used in the printing industry. Compressed (losing and lossless) and uncompressed formats are available. A variation of it, the GeoTiff contains georeferencing control points, therefore by reading it into a remote sensing system georeferenced data will be available immediately.

Jpg: An outstandingly efficient compressing algorithm has been used, which keeps rich details of images. It is widely used by digital photography. The amount of quality loss can be calibrated by the end-user. Of course, the larger the compression, the more image quality loss is produced. It is only used for storing rich detailed data, like photos. It is not suitable for compressing line-based raster formats, because the quality loss is significant. A georeferenced variation of it is Jpg2000, which is a lossless format, although has not been used widely.

Bil: This was specifically created for storing remote sensing images in binary format, capable of storing frequent data bands, calibration points, corner coordinates and projection system parameters. The *.bil file contains intensity value for each pixel by band, *.hdr header file contains attribute data, like georeferenced points, corner and column data. Widely spread and used by many remote sensing software programs, systems; moreover, vector-based systems capable of processing raster data are also supporting this type of raster format.

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