napp photo five pockets near dubois. google earth
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What is Geometric Correction?
Any process that changes the spatial characteristics of pixels. Pixel coordinates (e.g., map projection) Pixel relationship with other pixels Pixel size
Geometric correction also can change the digital numbers of pixels (resampling)
Why Geometric Correction?
To allow an image to overlay a map To eliminate distortion caused by
terrain, instrument wobble, earth curvature, etc.
To change the spatial resolution of an image
To change the map projection used to display an image
Two basic techniques for fitting images to maps
1. Use Ground Control Points (GCPs) to assign real-world coordinates to an image (rectification).
2. Create links between two images or between the image and a digital map to align them with one another (registration)Both techniques are based on same
concept.
Rectification Using GCPs
Object: To match pixel locations in the image to their corresponding locations on the earth
Method: Assign real-world coordinates to known
locations in the image (GCPs) Create a mathematical model to fit the
real-world coordinates to the image coordinates
“Warp” the image to fit the model.
Ground Control Points (GCPs)
Road intersections, river bends, distinct natural features, etc.
GCPs should be spread across image Requires some minimum number of
GCPs depending on the type of mathematical transformation (model) you use More usually better than fewer!
Some say that it is better to have clusters of GCPs spread across image
How is image registration different?
Instead of finding GCPs from a map, you link the same place on two or more images Can be used to georeference an
unreferenced image using a referenced image
Can be used to allow two images to perfectly line up with one another (e.g. images from the same place taken on different dates) even if they aren’t georeferenced
Two main steps necessary to fit an image to a map
1. Transformation: Use a mathematical equation to transform all image GCP coordinates to best match the real world GCP coordinates.
2. Resampling: Assign new DNs to the pixels once they have been moved to their new positions.
Image Coordinates
Real W
orld
Coord
inate
s
Mathematical Transformations
Points = GCPs; Line = best linear (1st order) fit
Mathematical Transformations
1st Order Requires minimum of 3 GCPs Use for small, flat areas
2nd Order Requires minimum of 6 GCPs Use for larger area where earth curvature is a
factor Use where there is moderate terrain Use with aircraft data where roll, pitch, yaw are
present
Mathematical Transformations (cont.)
3rd Order Requires minimum of 10 GCPs Very rugged terrain
Typically want at least 3x the minimum number of GCPs
Image Transformation (warping)
Raw Image
(No spatial relationship to location on earth)
Transformed Image
(Matches real-world coordinates; Oriented to north, etc.)
Resampling Techniques
Nearest Neighbor Assigns the value of the nearest pixel to
the new pixel location Bilinear
Assigns the average value of the 4 nearest pixels to the new pixel location
Cubic Convolution Assigns the average value of the 16
nearest pixels to the new pixel location
To maintain image radiometry (DNs) for spectral analysis ALWAYS USE NEAREST NEIGHBOR RESAMPLING!
If your purpose is to produce an image for presentation, bilinear or cubic convolution might work better (can be more visually pleasing).
Remember that EVERY TIME you resample an image for any reason you are altering the original data (DNs)!
Changing Image Spatial Resolution (A type of
Resampling) Two choices
Increase the resolution (artificially make pixels smaller)
Just assign the DN from the original pixel to the smaller pixels that fall inside it
Decrease the resolution (artificially make pixels larger)
Combine the DNs from the original pixels in some way (e.g. average them) to assign a new DN to the bigger pixel
Changing Map Projections
Map projections are mathematical schemes for depicting part of the spherical earth on a flat map or image
Every time you change from one map projection to another you transform and resample (and change the DNs!).
Geometric Correction -- Summary
Essential for almost all remote sensing projects
Critical for combining imagery and GIS
Essential for obtaining spatially accurate products—requires considerable care
Often done for us “at the factory,” but sometimes not, especially for aerial imagery (air photos, etc.)