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 & CalibrationSensor 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 2003http://landsat.usgs.gov/products_slc_off_data_information.php

SLC compensates for forward motion of the scanner during scan

Atmospheric Interference: cloudsclouds 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 CorrectionCorrected image scene orientation ‘map’ Uncorrected data ‘path’

Pixels and rows

Group discussion• Why is rectification needed for remote sensing images?

Why is rectification neededRaw 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 distortionsIn 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

RectificationData 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

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

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