1. NAPP Photo Five Pockets near Dubois

2. Google Earth

3. Geometric Corrections Rectification and Registration

4. What is Geometric Correction? Any process that changes the spatial characteristics of pixels. Any process that changes the spatial characteristics of pixels. Pixel coordinates (e.g., map projection) Pixel coordinates (e.g., map projection) Pixel relationship with other pixels Pixel relationship with other pixels Pixel size Pixel size Geometric correction also can change the digital numbers of pixels (resampling) Geometric correction also can change the digital numbers of pixels (resampling)

5. Why Geometric Correction? To allow an image to overlay a map To allow an image to overlay a map To eliminate distortion caused by terrain, instrument wobble, earth curvature, etc. To eliminate distortion caused by terrain, instrument wobble, earth curvature, etc. To change the spatial resolution of an image To change the spatial resolution of an image To change the map projection used to display an image To change the map projection used to display an image

7. 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.

8. Rectification Using GCPs Object: To match pixel locations in the image to their corresponding locations on the earth Object: To match pixel locations in the image to their corresponding locations on the earth Method: Method: Assign real-world coordinates to known locations in the image (GCPs) 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 Create a mathematical model to fit the real- world coordinates to the image coordinates “Warp” the image to fit the model. “Warp” the image to fit the model.

9. Real World Coordinate Frame (e.g., UTM) Image Coordinate Frame (row/column)

10. Ground Control Points (GCPs) Road intersections, river bends, distinct natural features, etc. Road intersections, river bends, distinct natural features, etc. GCPs should be spread across image GCPs should be spread across image Requires some minimum number of GCPs depending on the type of mathematical transformation (model) you use Requires some minimum number of GCPs depending on the type of mathematical transformation (model) you use More usually better than fewer! More usually better than fewer! Some say that it is better to have clusters of GCPs spread across image Some say that it is better to have clusters of GCPs spread across image

11. Google Earth – Seminoe Reservoir (Wyoming). Where would you locate GCPs?

12. How is image registration different? Instead of finding GCPs from a map, you link the same place on two or more images 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 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 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

13. 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.

14. Result of erroneous GCP coordinates or placement.

15. Image Coordinates Real World Coordinates Mathematical Transformations Points = GCPs; Line = best linear (1 st order) fit

16. Mathematical Transformations 1 st Order 1 st Order Requires minimum of 3 GCPs Requires minimum of 3 GCPs Use for small, flat areas Use for small, flat areas 2 nd Order 2 nd Order Requires minimum of 6 GCPs Requires minimum of 6 GCPs Use for larger area where earth curvature is a factor Use for larger area where earth curvature is a factor Use where there is moderate terrain Use where there is moderate terrain Use with aircraft data where roll, pitch, yaw are present Use with aircraft data where roll, pitch, yaw are present

17. Mathematical Transformations (cont.) 3 rd Order 3 rd Order Requires minimum of 10 GCPs Requires minimum of 10 GCPs Very rugged terrain Very rugged terrain Typically want at least 3x the minimum number of GCPs Typically want at least 3x the minimum number of GCPs

19. Image Transformation (warping) Raw Image (No spatial relationship to location on earth) Transformed Image (Matches real-world coordinates; Oriented to north, etc.)

20. Image Resampling Once an image is warped, how do you assign DNs to the “new” pixels? Once an image is warped, how do you assign DNs to the “new” pixels?

22. Resampling Techniques Nearest Neighbor Nearest Neighbor Assigns the value of the nearest pixel to the new pixel location Assigns the value of the nearest pixel to the new pixel location Bilinear Bilinear Assigns the average value of the 4 nearest pixels to the new pixel location Assigns the average value of the 4 nearest pixels to the new pixel location Cubic Convolution Cubic Convolution Assigns the average value of the 16 nearest pixels to the new pixel location Assigns the average value of the 16 nearest pixels to the new pixel location

23. Nearest Neighbor Resampling

24. Bilinear Resampling

25. Cubic Convolution Resampling

26. To maintain image radiometry (DNs) for spectral analysis ALWAYS USE NEAREST NEIGHBOR RESAMPLING! 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). 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)! Remember that EVERY TIME you resample an image for any reason you are altering the original data (DNs)!

27. Changing Image Spatial Resolution (A type of Resampling) Two choices Two choices Increase the resolution (artificially make pixels smaller) Increase the resolution (artificially make pixels smaller) Just assign the DN from the original pixel to the smaller pixels that fall inside it Just assign the DN from the original pixel to the smaller pixels that fall inside it Decrease the resolution (artificially make pixels larger) 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 Combine the DNs from the original pixels in some way (e.g. average them) to assign a new DN to the bigger pixel

28. Changing Map Projections Map projections are mathematical schemes for depicting part of the spherical earth on a flat map or image 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!). Every time you change from one map projection to another you transform and resample (and change the DNs!).

29. Geometric Correction -- Summary Essential for almost all remote sensing projects Essential for almost all remote sensing projects Critical for combining imagery and GIS Critical for combining imagery and GIS Essential for obtaining spatially accurate products—requires considerable care 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.) Often done for us “at the factory,” but sometimes not, especially for aerial imagery (air photos, etc.)