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Remote sensing image correction

Introductory readings – remote sensing

http://www.microimages.com/documentation/Tutorials/introrse.pdf

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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

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PreprocessingRadiometric 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 layersenable mosaicking and comparison

Radiometric correction: modification of DNs

Errors

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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

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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

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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.

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].

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Advanced slide: Reflectance to Radiance Conversion

DN reflectance values can be converted to absolute radiance values.

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.

Geometric CorrectionCorrected image scene orientation ‘map’ Uncorrected data ‘path’

Pixels and rows

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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)

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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 mountainsCorrecting with a DEM involves ‘orthorectification’

Altitude and attitude variations in satellite: these are minor

Geocorrection

Rectification – assigning coordinates to (~6) known locations - GCPs

GCP = Ground Control Point

Resampling - resetting the pixels (rows and columns) to match the GCPs

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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 InterpolationNew 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

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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

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

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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

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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)

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

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Ellipsoids and DatumsData will also have a datum:

NAD27: North American datum 1927NAD83: North American Datum 1983

There is a 100-200 metre difference between NAD27 and NAD83

NADCON83: NAD for continental USANAD83 Canada: based on Canadian landmassWGS84: World Geodetic System 1984

There is ‘very little’ difference between WGS84 and NAD83(flavours)

But ………………….. AIEEEEEEEEEE !

Reprojection – error stripes

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Reprojection – geographic (WGS84) to UTM / Albers

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

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Now for something completely different – perfect registration needed….

100% Marilyn Monroe -> 100% Margaret Thatcher