Remote Sensing - I

1. Remote Sensing - I Optical Range – Geometric Operations

2. Geometric Operations • Transforms and resamples data into any needed projection by various methods of different quality • Sensor 'a' to sensor 'b'; sensor to map grid; etc. • Preconditions are well distributed pass-points and/or GPS/INS/Giromeasurements

3. Orthorectification • Orthorectification, a subtopic of georeferencing, is the process of converting images into a form, suitable for maps, by removing sensor, satellite/aircraft motion and terrain-related geometric distortions from raw imagery. • Orthorectified data sets are required for thematic image analysis especially when using Geographic Information Systems (GIS): • for data fusion and analysis of data from different sources or seasons • when overlaying images with existing data sets and maps • for evaluations like change detection and updating maps 

4. Geometric Distortions of Data acquired from Space- and Airborne Platforms satellite skew (earth rotation) altitude variation pitch variation yaw variation roll variation velocity variation

5. Schematic of Scan Line Correction Direction of spacecraft motion (b) Correction for satellite or aircraft motion Field of view Mirror turnaround • Uncompensated scan lines • (oscillating mirror) (c) Compensated scan lines adapted from NASA

6. Spaceborne Data Non-Parametric Airborne Data Parametric EnMAP Orthorectification: Simulated Makhtesh Ramon (RGB 2200/800/450 nm) HyMAP Orthorectification: (RGB 2200/800/450 nm) Parameric / Non-parametric Approaches

7. Standard Geometric Correction Procedures for Satellite and Airborne Data (Ortho)Rectification Methods affine transformation polynomical transformation parametric approach (LoS - INS/GPS) Resampling Methods nearest neighbor bilinear interpolation cubic convolution

8. Original Image • Grade Polynomial • (affine transformation) 2. Grade Polynomial Scaling Rotation Shearing Arbitrary quadratic approximations Image Rectification with Polynomials

9. Fit by Polynom of 6. Order Fit by Polynom of 2. Order Fit by Polynom of 1. Order (affine) Geo-Referencingusingdifferent Polynoms for Fit X-Coordinate of Reference (e.g. TK 25) X-Coordinate of Satellite Image

10. Rule of thumb: Use a first-order polynomial (affine) whenever possible Select a higher-order polynomial only when there are severe geometric distortions Polynomial 1. Order x´ = a + a x + a y 0 1 2 y´ = b + b x + b y 0 1 2 Polynomial 2. Order 2 2 x´ = a + a x + a y + a x + a y + a xy 0 1 2 3 4 5 2 2 y´ = b + b x + b y + b x + b y + b xy 0 1 2 3 4 5 where x ´,y´ = Coordinates of source image (A) x , y = Coordi nates of image to be corrected (B) a , b = Vektors (including transformation coefficients) Non-Parametric Transformation Options

11. Pass Points to assign Polynomial Coefficients Desired Properties: • high contrast in imagery • small, subtle but prominent structure • temporarily unchanged • same height • uniformly distributed

12. N 12.30 noon local time N Space Oblique Mercator Projection (SOM) x (Rows) 98.2o Earth rotation Equator y (Lines) 9.42 am equatorial crossing time Image Coordinates [pixel] Near polar, sun-synchronous Orbit constructed using Ikonos imagery! Landsat Orbit Inclination & derived Image Coordinates

13. Potsdam Area PP6 PP1 PP5 PP2 compare resampling techniques based on Landsat imagery PP4 PP3 Selection of Pass Points in Imagery (Ikonos)

14. PP1 PP6 right wrong PP2 PP5 PP3 PP4 Identification of Imagery Pass Points in Map

15. = - + + X 931458 0 . 162893 * X 0 . 0321827 * Y Input Re f . Re f . = - - + Y 836932 0 . 031226 * X 0 . 168642 * Y Input Re f . Re f . Geometric Correction using a 1. Order Polynomial

16. GCP2 GCP4 GCP2 GCP4 Critical Example for Pass Point Selection

17. N Comparison unreferenced – referenced Image correction of skew and orbit inclination by affine transformation Y (Northing) x (Rows) y (Lines) X (Easting) Image Coordinates [pixel] World Coordinates [meter]

18. ? Image Rectified Image Nearest neighbor Bilinear interpolation Cubic convolution Resampling Options

19. Direct Rectification Indirect Rectification Image Rectified Image (Map) Image Rectified Image (Map) Transformation Equations: = + + X c 1 c 2 * X c 3 * Y = + + X a a * X a * Y Image Map Map Map 1 2 Image 3 Image = + + Y d 1 d 2 * X d 3 * Y = + + Y b b * X b * Y Image Map Map Map 1 2 Image 3 Image Direct and indirect Rectification

20. Landsat Imagery (30 m GSD) Nearest Neighbour non referenced Base Image Bi-linear Interpolation Cubic Convolution Resampling Techniques – Comparison of Results I

21. nearest neighbor bilinear interpolation cubic convolution nearest neighbor bilinear interpolation cubic convolution Resampling Techniques – Comparison of Results II Landsat Imagery (30 m GSD)

22. Parametric Geocoding • Required Parameters are sensor position, sensor attitude and pass points • For high dynamic vehicles such as aircraft, GPS and INS (Inertial Navigation System) have to be used complementary, as INS fills in the gaps between GPS positions. • The benefits of using GPS with an INS are that the INS may be calibrated by the GPS signals and that the INS can provide position and angle updates at a quicker rate than GPS. • Additionally, GPS may lose its signal and the INS can continue to compute the position and angle during the period of lost GPS signal.

23. Geometry of Aircraft Scanning   z (x0, y0, z0)   Roll Pitch Yaw   x source: NASA y

24. Parametric Correction Example: WAAC sensor imagery (courtesy: DLR) pitch roll Raw Image Rectified Image

25. Parametric Geocoding of Airborne Data geometrically corrected data Attitude angles (roll/pitch/heading) DGPS flight path Digital elevation model Ground control points geometrically uncorrected data