Image data are rasters, stored in a rectangular matrix of rows and columns. Radiometric resolution determines how many gradations of brightness can be stored for each cell (pixel) in the matrix; 8-bit resolution, where each pixel contains an integer value from 0 to 255, is most common. Modern sensors often collect data at higher resolution and advanced image processing software can make use of these values for analysis. The human eye cannot detect very small differences in brightness, and most GIS software can only read an 8-bit value.
In a grayscale image, 0 = black and 255 = white; and there is just one 8-bit value for each pixel. However, in a natural color image, there is an 8-bit value for red, an 8-bit brightness value for green, and an 8-bit value for blue. Therefore, each pixel in a color image requires 3 separate values to be stored in the file. There are three possible ways to organize these values in a raster file.
- BIP - Band Interleaved by Pixel: The red value for the first pixel is written to the file, followed by the green value for that pixel, followed by the blue value for that pixel, and so on for all the pixels in the image.
- BIL - Band Interleaved by Line: All of the red values for the first row of pixels are written to the file, followed by all of the green values for that row followed by all the blue values for that row, and so on for every row of pixels in the image.
- BSQ - Band Sequential: All of the red values for the entire image are written to the file, followed by all of the green values for the entire image, followed by all the blue values for the entire image.
Orthoimages are delivered in a variety of image formats, compressed and uncompressed. The most common are TIF and JPG. Compression eases data management challenges, as large high-resolution orthophoto projects can easily result in terabytes of uncompressed imagery. Compression can also speed display in GIS systems. The downside is that compression can introduce artifacts and change pixel values, possibly hampering interpretation and analysis, particularly with respect to fine detail. The decision to compress should be driven by end user requirements; it is not uncommon to deliver a set of uncompressed imagery for archival and special applications along with a set of compressed imagery for easy use by large numbers of users. If there is an intention for web-based display or distribution of orthoimagery, a compressed set of orthoimagery is often recommended. In any event, georeferencing information must also be provided. Both TIF and JPG image formats can accommodate georeferencing information, either imbedded in the image file itself, as in the case of GeoTIF, or as a separate file for each image, as in the case of TIF with a TFW (TIF World) file. The georeferencing information tells GIS software 1) the size of a pixel, 2) where to place one corner of the image in the real world, and 3) whether the image is rotated with respect to the ground coordinate system.
Other popular image formats you may encounter are:
- ECW: developed by ERMapper, now owned by ERDAS. Uses wavelet compression to reduce file size.
- GRID: developed by Esri; supported by some remote sensing software packages, but not as common as other formats.
- IMG: developed by ERDAS for Imagine; supported by many GIS and remote sensing software packages.
- JP2: JPEG 2000, developed by JPEG group. Widely supported by most GIS and remote sensing software packages.
- SID: MrSid, developed by Lizard Tech. Uses wavelet compression to reduce file size. Read by many software packages, but requires proprietary software license to create.