Understanding Optical Satellite Modeling

Optical data sets can be distributed as either sensor-oriented or map-oriented products. Sensor-oriented products are provided in the direction of the satellite track. Map-oriented products are resampled to correspond to a specific map projection and elevation.

To orthorectify map-oriented products, you must use ground control points (GCP). GCPs are often included in the image metadata, or you can enter them manually.

With sensor-oriented data sets; that is, those that have not been resampled to a map projection, the ability to relate image coordinates to geodetic positions and vice versa requires knowledge of the satellite position and viewing geometry. If the positional or viewing information is unreliable, the model can be improved when GCPs are available. Typically, the orbital and viewing geometry information is available in the metadata provided by the vendor.

When 20 or more well-distributed GCPs are available, you can derive the rational polynomial coefficients (RPC) needed to model the relationship between image coordinates and geodetic locations adjusted for elevation. With some sensors, the GCPs can be extracted from the vendor metadata. If unavailable, they must be derived from the orbital and viewing information or entered manually.

With PCI software, the orbital and viewing geometry for sensor-oriented data sets is extracted automatically. If available, GCPs, RPCs, or both are also stored. With resampled data products; that is, in map projection, PCI software extracts any GCPs, RPCs, or both supplied by the vendor. This ancillary information is stored as auxiliary segments.

The orbital and viewing information, GCPs, and RPCs are stored in the orbit, GCP, and binary segments, respectively. It is important to note that the metadata associated with the orbital and viewing geometry applies to the entire image. Therefore, you must not rely on the validity of the orbital-segment information when processing a subset of the original image or if the dimensions, orientation, or both have changed. However; GCPs and RPCs are modified automatically to adjust to changes in image dimensions.

When the orbital segment, which also contains viewing geometry information, is available, GCPs, RPCs, or both can be generated using math models.

The following descriptions refer to the options of the same name in the Project Information window.

Optical Satellite Modeling is designed to compensate for distortions, such as sensor geometry, satellite orbit and attitude variations, and Earth shape, rotation, and relief. This math model is based on the colinearity condition, which models the transformation between the image space and that of the ground. It uses principles related to photogrammetry, orbitography, geodesy, and cartography. The model accounts for the complete viewing geometry, and the distortions generated during image formation caused by the following:

Platform (position, velocity, and orientation)
Sensor (orientation, integration time, and field of view)
Earth (geoid, ellipsoid, and relief)
Cartographic projection (ellipsoid and cartographic)

Each unknown is the combination of several correlated variables of the viewing geometry, so the equations can be solved with few ground-control coordinates, tie points (TP), or both if more than one image is used. By using this model, you can create a project using multiple images acquired from one satellite or from a combination of images acquired from various satellites.

With full data sets in sensor orientation, you can use Optical Satellite Modeling if the orbital segment is available.

When you select Optical Satellite Modeling, the following options are available:

Toutin's Model
Rational Function (Extract from image)
Rational Function (Compute from GCPs)
Low Resolution

Toutin's Model is a rigorous model that compensates for known distortions to calculate the position and orientation of the sensor at the time of image acquisition. It is suitable for use with any optical satellite data, regardless of resolution, such as CARTOSAT, LANDSAT, or SPOT.

Rational Function (Extract from image) can be applied to any image data set. If RPCs are unavailable, they can be generated from the points stored in the GCP segment (see RFMODEL). The advantage of using Rational Function (Extract from image) is that you can apply it to images that have been modified, do not contain an orbit segment, or both. This math model is recommended for use with data sets in map orientation, image subsets, or those with no orbital information.

Rational Function (Compute from GCPs) can be applied to any image data set. If RPCs are unavailable, they can be generated from the points stored in the GCP segment (see RFMODEL). The advantage of using Rational Function (Compute from GCPs) is that you can apply it to images that have been modified, do not contain an orbit segment, or both. This math model is recommended for use with data sets in map orientation, image subsets, or those with no orbital information.

Low Resolution is for use with advanced very-high-resolution radiometer (AVHRR) data.

For more information, see: