Remote sensing image correction Introductory readings – remote sensing http://www.microimages.com/documentation/Tutorials/introrse.pdf 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 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 Radiometric correction: modification of DNs Errors 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 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 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 Landsat ETM+ scan line corrector (SLC) – failed May 31 2003 http://landsat.usgs.gov/products_slc_off_data_information.php SLC compensates for forward motion of the scanner during scan 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]. Geometric Correction Corrected image scene orientation ‘map’ Uncorrected data ‘path’ Pixels and rows Group discussion • Why is rectification needed for remote sensing images? 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 !! 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) Geocorrection Rectification – assigning coordinates to (~6) known locations - GCPs GCP = Ground Control Point Resampling - resetting the pixels (rows and columns) to match the GCPs 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 Resampling methods 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 http://www.geo-informatie.nl/courses/grs20306/course/Schedule/Geometric-correction-RS-new.pdf 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 Canadian Arctic mosaic See also google maps, lrdw.ca/imap etc.. Northern Land Cover of Canada – Circa 2000 http://ccrs.nrcan.gc.ca/optical/landcover2000_e.php Striping from projecting SRTM data, from Lat/long to UTM; Chile Now for something completely different – perfect registration needed…. 100% Marilyn Monroe -> 100% Margaret Thatcher