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Chapter 3

Chapter 3. Image Rectification Analysis and applications of remote sensing imagery Instructor: Dr. Cheng-Chien Liu Department of Earth Sciences National Cheng Kung University Last updated: 26 April 2005. Introduction. Why need Rectification ( 糾正) ? Distortion 畸變  Rectification 糾正

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Chapter 3

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  1. Chapter 3 Image Rectification Analysis and applications of remote sensing imagery Instructor: Dr. Cheng-Chien Liu Department of Earth Sciences National Cheng Kung University Last updated: 26 April 2005

  2. Introduction • Why need Rectification (糾正)? • Distortion 畸變 Rectification 糾正 • Geometric distortion 幾何畸變 • Altitude, attitude, velocity of sensor platform • Panoramic distortion, earth curvature, atmospheric refraction, relief displacement, nonlinearities in the sweep of a sensor’s IFOV

  3. Geometric correction • Two-step procedure • Systematic (predictable) • e.g. eastward rotation of the earth  skew distortion • Deskewing  offest each successive scan line slightly to the west  parallelogram image • Random (unpredictable) • e.g. random distortions and residual unknown systematic distortions • Ground control points (GCPs) • Highway intersections, distinct shoreline features,… • Two coordinate transformation equations • Distorted-image coordinate  Geometrically correct coordinate

  4. Two coordinate transformation equations • Affine coordinate transform • Six factors • Transformation equation • x = a0 + a1X + a2Y • y = b0 + b1X + b2Y • (x, y): image coordinate • (X, Y): ground coordinate • Six parameters  six conditions  3 GCPs • If GCPs > 3  redundancy  LS solutions

  5. Resampling • Resampling • Fig 7.1: Resampling process • Transform coordinate • Adjust DN value  perform after classification • Methods • Nearest neighbor • Bilinear interpolation • Bicubic convolution

  6. Resampling (cont.) • Nearest neighbor • Fig 7.1: a  a΄ (shaded pixel) • Fig C.1: implement • Rounding the computed coordinates to the nearest whole row and column number • Advantage • Computational simplicity • Disadvantage • Disjointed appearance: feature offset spatially up to ½ pixel (Fig 7.2b)

  7. Resampling (cont.) • Bilinear interpolation • Fig 7.1: a, b, b, b  a΄ (shaded pixel) • Takes a distance-weighted average of the DNs of the four nearest pixels • Fig C.2a: implement • Eq. C.2 • Eq. C.3 • Advantage • Smoother appearing (Fig 7.2c) • Disadvantage • Alter DN values • Performed after image classification procedures

  8. Resampling (cont.) • Bicubic (cubic) interpolation • Fig 7.1: a, b, b, b, c, …  a΄ (shaded pixel) • Takes a distance-weighted average of the DNs of the four nearest pixels • Fig C.2b: implement • Eq. C.5 • Eq. C.6 • Eq. C.7 • Advantage (Fig 7.2d) • Smoother appearing • Provide a slightly sharper image than the bilinear interpolation image • Disadvantage • Alter DN values • Performed after image classification procedures

  9. Exercise 1 • Georeferenced Data and Image-Map • Construct an image-map complete with map grids and annotation, and produce an output image • Start ENVI • Open and Display SPOT Data • bldr_reg subdirectory: bldr_sp.img • Edit Map Info in ENVI Header • Edit Map Information • The basic map information used by ENVI in georeferencing. • Click on the arrow next to the Projection/Datum field • Click on the active DMS or DDEG button • Cursor Location/Value

  10. Exercise 1 (cont.) • Overlay Map Grids • Overlay →Grid Lines. • File →Restore Setup • file bldr_sp.grd • Options →Edit Map Grid Attributes • Options →Edit Geographic Grid Attributes • Apply • Overlay Map Annotation • Overlay →Annotation • File →Restore Annotation • file bldr_sp.ann • Object • Output to Image or Postscript • Direct Printing

  11. Exercise 2 • Image to Image Registration • The georeferenced SPOT image will be used as the Base image, and a pixel-based Landsat TM image will be warped to match the SPOT. • Open and Display Landsat TM Image File • bldr_reg directory: file bldr_tm.img • Band 3 • Display the Cursor Location/Value • Start Image Registration and Load GCPs • Map → Registration → Select GCPs: • Base Image: Display #1 (the SPOT image) • Warp Image: Display #2 (the TM image). • SPOT image to 753, 826 • TM image to 331, 433 • Add Point • Show List • Try this for a few points to get the feel of selecting GCPs. Once you have at least 4 points, the RMS error is reported. • Options → Clear All Points to clear all of your points.

  12. Exercise 2 (cont.) • File → Restore GCPs from ASCII. • file name bldr_tm.pts • Working with GCPs • On/Off • Delete • Update • Predict • Warp Images • Options → Warp • Displayed Band. • Warp Method • RST • Resampling • Nearest Neighbor • filename bldr_tm1.wrp • repeat steps 1 and 2 still using RST warping but with both Bilinear, and Cubic Convolution resampling methods. • Output the results to bldr_tm2.wrp and bldr_tm3.wrp, respectively. • Repeat steps 1 and 2 twice more, this time performing a 1st degree Polynomial warp using Cubic Convolution resampling, and again using a Delaunay Triangulation warp with Cubic Convolution resampling. • Output the results to bldr_tm4.wrp and bldr_tm5.wrp, respectively.

  13. Exercise 2 (cont.) • Compare Warp Results • Tools → Link → Link Displays • Load bldr_tm2.wrp and bldr_tm3.wrp into new displays and use the image linking and dynamic overlays to compare the effect of the three different resampling methods: nearest neighbor, bilinear interpolation, and cubic convolution. • Note how jagged the pixels appear in the nearest neighbor resampled image. The bilinear interpolation image looks much smoother, but the cubic convolution image is the best result, smoother, but retaining fine detail. • Examine Map Coordinates • Tools → Cursor Location/Value • Close All Files

  14. Exercise 3 • Image to Map Registration • The map coordinates picked from the georeferenced SPOT image and a vector Digital Line Graph (DLG) will be used as the Base, and the pixel-based Landsat TM image will be warped to match the map data. • Open and Display Landsat TM Image File • File → Open Image File. • bldr_reg directory: file bldr_tm.img • Gray Scale • Band 3

  15. Exercise 3 (cont.) • Select Image-to-Map Registration and Restore GCPs • Map → Registration → Select GCPs: • Image to Map • UTM • enter 13 in the Zone text field. • Leave the pixel size at 30 m and click OK to start the registration. • Add Individual GCPs by moving the cursor position in the warp image to a ground location for which you know the map coordinate (either read from a map or ENVI vector file [see the next section]). • Enter the known map coordinates manually into the E (Easting) and N (Northing) text boxes and click Add Point to add the new GCP. • File → Restore GCPs from ASCII • file bldrtm_m.pts. • Show List

  16. Exercise 3 (cont.) • Select Image-to-Map Registration and Restore GCPs • Add Map GCPs Using Vector Display of DLGs • File → Open Vector File → USGS DLG. • bldr_rd.dlg • Memory • ROADS AND TRAILS: • BOULDER, CO file in the Available Vectors Layers • Load Selected • New Vector Window • Click and drag the left mouse button in the Vector Window #1 to activate a crosshair cursor. • Tools → Pixel Locator • 402, 418 • Apply. • In the Vector Window 477593.74, 4433240.0 • Select Export Map Location. The new map coordinates will appear in the Ground Control Points Selection dialog. • Add Point • observe the change in RMS error

  17. Exercise 3 (cont.) • RST and Cubic Convolution Warp • Options →Warp File • file name bldr_tm.img • select all 6 TM bands for warping. • Warp Method RST • Resampling Cubic Convolution • background value 255 • output file name bldrtm_m.img • Display Result and Evaluate • Close Selected Files

  18. Self test • Conduct the image-to-image registration • Base image: • C:\RSI\IDL60\examples\data\afrpolitsm.png • Warp image: • C:\RSI\IDL60\examples\data\africavlc.png • Pay special attention to • Selection of GCPs • No of GCPs • Value of RMS • Method of warping • Examine your result by linking two displays with 50% transparency

  19. Orthorectification • Orthorectification • Definition • The geometry of an image is made planimetric (map-accurate) by modeling the nature and magnitude of geometric distortions in the imagery • Steps • Build the interior orientation (aerial photograph only) • Build the exterior orientation • Orthorectify using a Digital Elevation Model (DEM)

  20. Exercise 4 • Orthorectify the airphoto of Cha-Yi area • Raw image: 88R56151.tif • Build the interior orientation • Focal Length and fiducial pairs: RMK-TOP30-AF.cam • Build the exterior orientation • Image of GCPs: DETAIL directory • Coordinates of GCPs: xyz.con • Orthorectify using a Digital Elevation Model • DEM file: dtm_40m.tif • Interpolation: 40m → 4m • Accuracy of orthoimage • Check points • Overlay vector files

  21. Exercise 5 • Georeferencing Images Using Input Geometry • Modern sensors → detailed acquisition (platform geometry) information → model-based geometric rectification and map registration • Users must have the IGM or GLT file as a minimum to conduct this form of geocorrection • The Input Geometry (IGM) file: the X and Y map coordinates for a specified map projection for each pixel in the uncorrected input image. • The Geometry Lookup (GLT) file: the sample and line that each pixel in the output image came from in the input image. • If the GLT value is positive, there was an exact pixel match. If the GLT value is negative, there was no exact match and the nearest neighboring pixel is used

  22. Exercise 5 (cont.) • Uncorrected HyMap Hyperspectral Data • HyMap • Aircraft-mounted commercial hyperspectral sensor • 126 spectral channels covering the 0.44 - 2.5 mm range with approximately 15nm spectral 162 resolution and 1000:1 SNR over a 512-pixel swath. Spatial resolution is 3-10 m • Gyro-stabilized platform • Open HyMap data • envidata/cup99hym directory • File: cup99hy_true.img • Examine Uncorrected Data • Cursor Location/Value • Examine IGM files • envidata/cup99hym directory • File: cup99hy_geo_igm • Available Bands List dialog • Gray Scale • IGM Input X Map • New Display • IGM Input Y Map • New Display

  23. Exercise 5 (cont.) • Uncorrected HyMap Hyperspectral Data (cont.) • Geocorrect Image Using IGM File • Map →Georeference from Input Geometry →Georeference from IGM • File: cup99hy.eff • Input Data File • File: cup99hy.eff • Spectral Subset • File Spectral Subset: band 109 • Input Data File • Input X Geometry Band: IGM Input X Map • Input Y Geometry Band: IGM Input Y Map • Geometry Projection Information • UTM, Zone 13, datum: North America 1927 • the same map projection as the input geometry. • Build Geometry Lookup File Parameters • background value of -9999, output filename • Display and Evaluate Correction Results • Available Bands List • Georef band • Cursor Location/Value • Examine GLT Files • GLT Sample Look-up • GLT Line Look-up

  24. Exercise 5 (cont.) • Geocorrect Image using GLT File • Map →Georeference from Input Geometry →Georeference from GLT • Input Geometry Lookup File: cup99hy_geo_glt • Input Data File: cup99hy.eff • Spectral Subset • File Spectral Subset: band 109 • Input Data File • Georeference from GLT Parameters -9999 • output filename • Display and Evaluate Correction Results • Available Bands List • Georef band. • Cursor Location/Value

  25. Exercise 5 (cont.) • Using Build GLT with Map Projection • File →Open Image • File: cup99hy_geo_igm • Input X Geometry Band • IGM Input X Map • Input Y Geometry Band • IGM Input Y Map • Geometry Projection Information • State Plane (NAD 27) • Set Zone • Nevada West (2703) • Build Geometry Lookup File Parameters • Overlay Map Grids

  26. Exercise 6 • IKONOS and QuickBird Orthorectification • Orthorectification • Use the Rational Polynomial Coefficients (RPCs) provided by the data vendors with some products • Orthorectification 正射糾正 • Open files • File → Open Image File • ortho subdirectory • File: po_101515_pan_0000000.tif • File → Open External File → Digital Elevation → USGS DEM • File: CONUS_USGS.dem • USGS DEM Input Parameters dialog • output filename: ortho_dem.dat • New Display • Load Band

  27. Exercise 6 (cont.) • Run the Orthorectification • Map → Orthorectification → Orthorectify IKONOS. • File: po_101515_pan_0000000.tif • Enter Orthorectification Parameters dialog • Image Resampling: Bilinear • Background 0 • Input Height • specifies whether a fixed elevation or a DEM (more accurate) value will be used for the entire image • ortho_dem.dat • DEM Resampling • Bilinear • Geoid Offset • The height of the geoid above mean sea level in the location of the image. • -35: means that the ellipsoid is about 35 meters above mean sea level in this area • Many institutions doing photogrammetric processing have their own software for geoid height determination, or you can obtain software from NOAA, NIMA, USGS, or other sources. A geoid height calculation can currently be found at the following URL: http://www.ngs.noaa.gov/cgi-bin/GEOID_STUFF/geoid99_prompt1.prl • Save Computed DEM • Orthorectified Image • File: ikonos_ortho.dat

  28. Exercise 6 (cont.) • Examine the Orthorectification Results • Tools → Link Displays → Link • Notice the difference in geometry, especially in the upper right corner of the two images. That is the result of the orthorectification process

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