lecture pre processing bogor 2007

Asia Link Project FORRSA

Pre-Processing of Remote Sensing DataBogor Agricultural University (IPB)

19.08 31.08.2007

Uwe Ballhorn

GeoBio-Center Ludwig-Maximilians-Universitt, Munich, Germany&

RSS - Remote Sensing Solutions GmbH, Munich, Germany

Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia 19.08.2007 31.08.2007 1

Content1. Image Processing Workflow

2. What is Prep-Processing?

3. Types of Pre-Processing3.1. Geometric Correction3.2. Radiometric Correction3.3. Noise Removal3.4. Georeferencing

4. Results of Pre-Processing


1. Image Processing Workflow

A. Pre-Processing: eliminate data registration errors geometric correction:

earth rotation earth curvature instability of the platform (altitude, velocity, pitch, roll and yaw) topographic effects

radiometric correction noise removal georeferencing



B. Image Enhancement: change the visual impression visualisation

RGB display / MCGB printer output false colour image concept

contrast stretching linear/ non linear histogramm manipulations

filter operations : change intensity according to surrounding pixel



C. Image Analysis principal component calculation indices calculations

ratios f.e. simple ratio = band 1/ band 2vegetation indices like NDVI = band 1 band 2 /

band 1 + band 2 multivariate data analysis (traditional) supervised classification unsupervised classification object oriented image analysis

D. Accuraccy Assessment


2. What is Pre-Processing?

correct distorted or degraded data

create a more faithful representation of the original scene

typically involves the initial processing of raw image data to correct for: geometric distortions calibrate the data radiometrically eliminate noise present in the data

image restoration process highly dependent upon the characteristics ofthe sensor used

normally precede further manipulation and analysis of the image data toextract specific information


3. Types of Pre-Processing

3.1. Geometric Correction

3.2. Radiometric Correction

3.3. Noise Removal

3.4. Georeferencing



raw digital images usually contain geometric distortions so that they cannotbe used directly as a map base without subsequent processing

sources of geometric distortions: variations in the altitude, attitude, and velocity of the sensor platform

panoramic distortion earth rotation earth curvature atmospheric refraction relief displacement nonlinearities in the sweep of a sensors IFOV etc.



Earth Curvature Earth Rotation

The effect of earth rotation on scanner imagery:

a. Image formed by lines arranged in a square gridb. Offset of successive lines to the west to correct for

the rotation of earths surface during the frame acquisition time

Effect of earth curvature on the size of a pixel in the scan direction



type of radiometric correction varies widely among sensors

sources of radiometric distortions: changes in scene illumination atmospheric conditions viewing geometry instrument response characteristics

need to perform correction for any or all of these influences dependsdirectly upon the particular application at hand



Examples

Sun elevation correction:accounts for the seasonal position of the sun relative to the earthimage data acquired under different illumination angles are normalized by calculating pixel brightness values assuming the sun was at the zenith on each date of sensing

Earth-sun distance correction:applied to normalize for the seasonal changes in the distance between the earth and the sun

Haze compensation:procedures designed to minimize the influence of path radiance effects


3.3. Noise Removal

unwanted disturbance in image data due to limitations in the sensing, signaldigitization, or data recording process

sources of noise: periodic drift or malfuction of a detector electronic interference between sensor components intermitted hiccups in the data transmission and recording sequence etc.

can either degrade or totolly mask the true radiometric information content

noise removal usually precedes any subsequent enhancement or classifiaction process

objective is to restore an image to as close to the original scene as possible


3.3. Noise Removal

Result of applying noise reduction algorithm:

a: original image data with noise induced salt andpepper appearance

b: image resulting from application of the filter algorithm in (c)

(from Lillesand and Kiefer, 1999)

Typical noise correction algorithm employing a 3x3 neighbourhood. WEIGHT is an analyst specified weighting factor. The lower the weight, the greater the number of pixels considered


3.4. Georeferencing

another important domain in pre-processing is georeferencing

geographic location is the element that distinguishes geographicinformation from all other types

methods for specifying location on the earths surface are essentialto the creation of useful geographic information

georeferenced images contain information concerning spatiallocation and pixel size, so that statements regarding distanceand area can be given

often image data is not correctly georeferenced

with the help of different data the spatial location can be addressedto image data


3.4. Georeferencing

Goal of the exercise

Georeferencing of a Landsat scene which has no information concerning spatial location. In this exercise another scene from the same area is used as base for the georeferencing process. Especially

for change detection in vegetation cover it is important that two satellite scenes fit exactly over each other. So this is an important

step in pre-processing remote sensing data.


4. Results of Pre-Processing

Data representing physical radiationmeasurements (radiometric calibration)

Data fitting into the geographic referencesystem chosen as base for the investigation

or the administrative GIS data holdingenvironment (geometric rectification)

Data base for image analysis


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lecture pre processing bogor 2007

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