Mosaic Method
lRun block correlation between all raw images and EarthSat's GeoCover Stock Mosaic
–Co-register raw images to GeoCover’s band 2
lRegister all categorized scenes to GeoCover base
–Use calibration coefficients
lMosaic all categorized images
–Establish scene overlap priorities
–Clip by scene edge or county boundary
–Mask out clouds via priority schemes
lExport to ERDAS’s .LAN image format
–Distribute in GeoTiff format
•Each GeoCover mosaic image covers an area approximately six degrees of latitude by six degrees of longitude.
•To completely cover a state and the Landsat scene footprints around a state, multiple GeoCover mosaics are used.
•The GeoCover handling steps are 1)  Imported into Erdas Imagine 2) Reduced to band two only 3) Resampled from 28.5 to 30 meters 4) Reprojected to one common UTM zone per state 5) Stitched together with other images to cover the state footprint 6) Exported to .LAN format.
•All CDL products are currently processed entirely by PEDITOR
•Finalized mosaics are exported to Geotiff format upon completion.
•PEDITOR’s Batch program routines process the scenes in as little as a few hours for the simple mosaics, and can run up to 2/3 a day on a large/complex state with many seams/stitch lines/cloud problems.
•The Landsat TM/ETM+ scenes used are radiometrically and systematically corrected.  There is a need to tie down registration points on a continuing basis for every scene within each state in the project.  Without some image/image registration, the scene registration tends to float 2-3 pixels in any given direction, for any given scene.
•An automated registration method was developed to co-register each raw scene to an ortho-base image, and then to apply each raw scene’s correlation coefficients to the categorized scenes.
•Image recoding is necessary between different analysis districts, to rectify to a common signature set for a state.
•Once an Analysis District is categorized, it possess a unique set of signatures.  These signatures are recoded to a master signature set.  For instance, a corn signature or category may have between 1 and 50 classes for a given Analysis District, and they are condensed into one class or digital number for the final mosaic.  This is done for every cover type in the project.
•Clouds pose a big problem when trying to make acreage estimates, and there are mechanisms within PEDITOR to minimize their extent, as there are ways to minimize cloud coverage in the mosaic process by prioritizing scene overlap.
•Each categorized scene needs to be geo-registered to an ortho-base image. A block correlation is run between band two from each raw scene, and band two of the ortho-base image. The registration of the GeoCover mosaicked scene and the individual raw input scenes are used to get an approximate correspondence.  A correlation procedure is used on the raw Landsat scenes and the mosaicked scene to get an exact mapping of each pixel from the input Landsat scenes to the mosaicked scene.  The results of the correlation are used to remap the pixels from the individual input scenes into the coordinate system of the mosaicked scene.
•The mosaic process now performs:  1)  Automates image registration/rectification, 2)  Converts each categorized image and associated statistics file to a set standard automatically (recode),  3)  Specifies overlap priority by scene or county,  4)  Filters out clouds when possible
•Each scene is co-registered to EarthSat's GeoCover LC imagery (50 meters RMS), and then stitched together using the priorities previously assigned from the scene observation dates/analysis districts map.
•Scenes/analysis districts with better quality observation dates are assigned a higher priority when stitching the images together.
•Clouds are assigned a null value on all scenes, and scenes of lower priority that are cloud free, take precedence over cloudy higher priority images.
•Once cloud cover is established throughout the mosaic the clouds are assigned a digital value.