Beginning with GeoEye’s IKONOS satellite, all leading high resolution commercial imaging satellites capture 11-bit imagery, meaning a maximum of 2,048 digital numbers (DNs) per band instead of the earlier 8-bit/256 levels. RapidEye’s satellite constellation and Astrium’s Pléiades satellite offer the additional advantage of collecting 12-bit imagery. Improved bit depth aids the ability to discern detail in an image’s brightest and darkest (shadow) areas.
Most computers require data in 8-bit format, so an 11- or 12-bit image can have the color table (DNs) downsampled to 8-bit or upsampled to 16-bit. In this case, the four or five unused bit locations are filled with zeros, creating a 16-bit file format but not a true 16-bit image. Imagery used for classification or analysis, or that which must be tonally balanced, always should be ordered in 16-bit format. For many users, however, there’s convenience to using 8-bit imagery— smaller file sizes and no software compatibility issues.
Dynamic Range Adjustment
When delivering 8-bit imagery, high-resolution satellite operators typically will perform an automated process to adjust contrast and brightness. Dynamic range adjustment (DRA) offers a time savings for “load-and-go” imagery, so a user can avoid running a manual adjustment. This is prone to fail in certain areas, however, such as a desert where the color spectrum isn’t balanced. In such instances, a manual adjustment will yield better results. Manual contrast adjustment also tends to work better with pan imagery.
Although many programs now perform “on-the-fly” reprojections, ordering imagery in the same projection as other project data being used is still desirable. Stereo imagery for DEM generation often will be ordered as epipolar; however, a Universal Transverse Mercator (UTM) ortho later can be output. Because satellite operators offer a limited selection of projections, supporting a region-specific datum may require a custom reprojection.
7. Resampling Method
Cubic convolution typically is the default resampling method for Earth imagery, but the enhanced kernel—a hybrid of cubic convolution—is recommended for DigitalGlobe’s pan-sharpened products. For research applications, where image data may be converted to radiance values, nearest-neighbor resampling offers the advantage of not introducing any new values to the imagery, but it can introduce what appear to be geometric and color defects. Most imagery users wouldn’t be satisfied with the appearance of imagery processed with the nearest-neighbor method.
Vector Feature Extraction
Vector extraction from imagery allows roads, hydrology, building
footprints and other features to be mapped faster and less expensively than traditional ground surveys to create new maps or update/
correct existing maps. The resulting accurate, up-to-date base data then are used to support applications such as Internet mapping
portals and handheld Global Positioning System devices.
Unlike classification maps, which require multispectral imagery to emphasize the spectral properties of various features, feature extraction
is based on spatial properties—size and shape of objects—so pan imagery can be used. Color imagery, however, makes it easier to identify certain
features, such as water, paved vs. unpaved roads, etc.
Vector extraction allows roads, hydrology and other features
to be mapped using satellite imagery.
Despite advancements with machine learning and object-based
image analysis, features such as roads and building footprints still
often are captured via manual heads-up digitizing. Computer-aided
design (CAD) applications typically work best with 8-bit imagery, with
a contrast stretch already applied to the imagery. Especially in areas of
interest (AOIs) of high relief or those comprising more than one scene,
imagery should be orthorectified prior to feature extraction.
Wavelet compressed file formats, such as ECW, MrSID and JPEG
2000, help facilitate file transfer across multiple production/quality
assurance teams. Otherwise, it may help to tile GeoTiff imagery, as
some CAD programs are limited in their ability to work with large
raster files. Whenever possible, seasonality consideration should be
given to AOIs, as leaf-off imagery yields better feature visibility.