1. Remote sensing image correction

2. Introductory readings – remote sensing http://www.microimages.com/documentation/Tutorials/introrse.pdf

4. Preprocessing Digital Image Processing of satellite images can be divided into:  Pre-processing  Enhancement and Transformations  Classification and Feature extraction Preprocessing consists of: radiometric correction and geometric correction

5. Preprocessing Radiometric Correction: removal of sensor or atmospheric 'noise', to more accurately represent ground conditions - improve image‘fidelity’:  correct data loss  remove haze  enable mosaicking and comparison Geometric correction: conversion of data to ground coordinates by removal of distortions from sensor geometry  enable mapping relative to data layers  enable mosaicking and comparison

6. Radiometric correction: modification of DNs Errors

7. Radiometric correction Radiometric correction is used to modify DN values to account for noise, i.e. contributions to the DN that are a result of… a.the intervening atmosphere b. the sun-sensor geometry c. the sensor itself – errors and gaps

8. Radiometric correction We may need to correct for the following reasons: a.Variations within an image (speckle or striping) b. between adjacent / overlapping images (for mosaicing) c. between bands (for some multispectral techniques) d. between image dates (temporal data) and sensors

9. Errors: Sensor Failure & Calibration Sensor problems show as striping or missing lines of data: Missing data due to sensor failure results in a line of DN values - every 16th line for TM data.. As there are 16 sensors for each band, scanning 16 lines at a time (or 6th line for MSS). MSS 6 line banding – raw scan MSS 6 line banding - georectified TM data – 16 line banding Sample DNs – shaded DNs are higher

10. Landsat ETM+ scan line corrector (SLC) – failed May 31 2003 http://landsat.usgs.gov/products_slc_off_data_information.php http://landsat.usgs.gov/products_slc_off_data_information.php SLC compensates for forward motion of the scanner during scan

11. Atmospheric Interference - haze http://geology.wlu.edu/harbor/geol260/lecture_notes/Notes_rs_haze.html Lower wavelengths are subject to haze, which falsely increases the DN value. The simplest method is known as dark object subtraction which assumes there is a pixel with a DN of 0 (if there were no haze), e.g. deep water in near infra-red. An integer value is subtracted from all DNs so that this pixel becomes 0.

12. Atmospheric Interference: clouds clouds affect all visible and IR bands, hiding features twice: once with the cloud, once with its shadow. We CANNOT eliminate clouds, although we might be able to assemble cloud-free parts of several overlapping scenes (if illumination is similar), and correct for cloud shadows (advanced). [Only in the microwave, can energy penetrate through clouds].

13. Advanced slide: Reflectance to Radiance Conversion DN reflectance values can be converted to absolute radiance values.radiance This is useful when comparing the actual reflectance from different sensors e.g. TM and SPOT, or TM versus ETM (Landsat 5 versus 7) DN = aL + b where a= gain and b =n offset The radiance value (L) can be calculated as: L = [Lmax - Lmin]*DN/255 + Lmin where Lmax and Lmin are known from the sensor calibration. This will create 32 bit (decimal) values.

14. Geometric Correction Corrected image scene orientation ‘map’ Uncorrected data ‘path’ Pixels and rows

15. Group discussion Why is rectification needed for remote sensing images?

16. Why is rectification needed Raw remote sensing data contain distortions preventing overlay with map layers, comparison between image scenes, and with no geographic coordinates  To provide georeferencing  To compare/overlay multiple images  To merge with map layers  To mosaic images e.g. google maps / google earth *** Much imagery now comes already rectified … YEAH !!

17. Image distortions In air photos, errors include: topographic and radial displacement; airplane tip, tilt and swing (roll, pitch and yaw). These are less in satellite data due to altitude and stability. The main source of geometric error in satellite data is satellite path orientation (non-polar)

18. Sources of geometric error (main ones in bold) a.Systematic distortions Scan skew: ground swath is not normal to the polar axis – along with the forward motion of the platform during mirror sweep Mirror-scan Velocity and panoramic distortion: along-scan distortion (pixels at edge are slightly larger). This would be greater for off-nadir sensors. Earth rotation: earth rotates during scanning (offset of rows).... (122 pixels per Landsat scene) b. Non-systematic distortions Topography: requires a DEM, otherwise ~ 6 pixel offset in mountains Correcting with a DEM involves ‘orthorectification’ Altitude and attitude variations in satellite: these are minor

19. Geocorrection Rectification – assigning coordinates to (~6) known locations - GCPs GCP = Ground Control Point Resampling - resetting the pixels (rows and columns) to match the GCPs

20. Rectification Data pixels must be related to ground locations, e.g. in UTM coordinates Two main methods: - Image to image (to a geocorrected image).... to an uncorrected image would be 'registration' not rectification -Image to vectors (to a digital file).... (black arrows point to known locations - coordinates from vectors or images) Ortho-rectification = this process (since ~2000) enables the use of a DEM to also take into account the topography

21. Resampling methods http://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdf New DN values are assigned in 3 ways a.Nearest Neighbour Pixel in new grid gets the value of closest pixel from old grid – retains original DNs b. Bilinear Interpolation New pixel gets a value from the weighted average of 4 (2 x 2) nearest pixels; smoother but ‘synthetic’ c. Cubic Convolution (smoothest) New pixel DNs are computed from weighting 16 (4 x 4) surrounding DNs

22. Resampling – pixel size Previously during resampling stage, pixels were rounded to match UTM grid and DEMs: Landsat MSS 80m raw pixels -> 50m corrected pixels Landsat TM 30 (28.5) m -> 25m BC TRIM DEM was built to 25m to match Landsat TM data New millenium software can handle layers with different resolution, so downloaded TM scenes are mostly 30m pixels

23. Resampling http://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdf Good rectification is required for image registration – no ‘movement between images

24. Canadian Arctic mosaic See also google maps, lrdw.ca/imap etc..

25. Northern Land Cover of Canada – Circa 2000 http://ccrs.nrcan.gc.ca/optical/landcover2000_e.php

26. Projections and reprojection  Global data might be downloaded as geographic (lat/long) or UTM zone  BC data as UTM or BC Albers  GIS and DIP software can display different projections ‘on the fly’  …but require reprojection for analysis and data overlay  Reprojecting vectors simply reassigns coordinates to points  Reprojecting rasters involves resampling every pixel (using nearest neighbour, bilinear or cubic convolution)

27. Release of new ASTER Global DEM (GDEM v2) – 3 Oct 2011 http://www.nasa.gov/topics/earth/features/aster20111017.html Available in Geographic (Lat/Long) or UTM zone

28. Ellipsoids and Datums Data will also have a datum:  NAD27: North American datum 1927  NAD83: North American Datum 1983  There is a 100-200 metre difference between NAD27 and NAD83  NADCON83: NAD for continental USA  NAD83 Canada: based on Canadian landmass  WGS84: World Geodetic System 1984 There is ‘very little’ difference between WGS84 and NAD83(flavours) But ………………….. AIEEEEEEEEEE !

29. Reprojection – error stripes

30. Reprojection – geographic (WGS84) to UTM / Albers

31. Striping from projecting SRTM data, from Lat/long to UTM; Chile

32. Now for something completely different – perfect registration needed…. 100% Marilyn Monroe -> 100% Margaret Thatcher