TASK 1

PREPROCESSING

(Geometric Correction, Atmospheric

Correction, Radiometric Correction,

Normalization)

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1.1 INTRODUCTION

1.1.1 Preprocessing

Preprocessing is to correct the geometric of image, eliminate data registration error and

creates a more faithful representation of the original scene. Preprocessing is an important and

diverse set of image preparation programs that act to offset problems with the band data and

recalculate DN values that minimize these problems. Among the programs that optimize these

values are atmospheric correction (affecting the DNs of surface materials [making them higher or

lower] because of radiance from the atmosphere itself, involving attenuation and scattering); sun

illumination geometry; surface-induced geometric distortions; spacecraft velocity and attitude

variations (roll, pitch, and yaw); effects of earth rotation, elevation, curvature (including skew

effects), abnormalities of instrument performance (irregularities of detector response and scan mode

such as variations in mirror oscillations); loss of specific scan lines (requires destriping), and others.

Once performed on the raw data, these adjustments may require appropriate radiometric and

geometric corrections.

1.2 OBJECTIVE

1. To carry out the preprocessing technique on Landsat TM and ETM+ acquired on 2001 and 2004 respctively.

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1.3 METHODOLOGY OF THE STUDY

Figure 1: Flowchart of the study.

Data Collection

The work must be planned and arranged properly. From metadata of the image, the important

information such as path/row, sun elevation, date, time, Lmin, Lmax, QCAL, QCALmax will be

used for radiometric and atmospheric correction.

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Data CollectionData: Topography map, Landsat 5 TM image, Landsat 7 ETM, metadata of the image

Software: ERDAS ImagineExcel software

Pre -processingGeometric correction: - GC Topography map - GC Landsat 5 TM image by using image to map rectification- Resampling

Atmospheric and radiometric correction: Cosine of Solar Zenith Angle Correction (COST) method referred by Chavez 1996

Normalization

1.3.1 Geometric Correction

The two types of geometric correction are image to map and image to image. Image to map

is the process by which the geometry of an image is made planimetric and this process normally

involves selecting GCP image pixel coordinates with their map coordinate counterparts. Image to

image registration is the matching of one image to another so the same geographic area is

positioned coincident with respect to the other. This type of geometric correction is used when it is

not necessary to have each pixel assigned a unique X, Y coordinate in a map projection.

Geometric correction includes the following steps :

Choosing a map coordinate system

Transforming the raster with a geometric model

Resampling the raster

1.3.1.1 Geometric Correction of Topography Map

Area of interest (AOI) is selected initially before performing the geometric correction process.

Geometric correction process:

Data preparation → image geometric correction → set geo correction input file → set geometric

model → add/change projection

Different geometric models exist to transform raster images to map coordinates. The image

resolution, the scale of the reference (map, vector file or georeferenced image) and the relief of the

area are to be considered when choosing a geometric model. For medium resolution image on flat

areas, the polynomials models are sufficient. In the case of images with large geometric distortions,

the orthorectification method is necessary in order to reduce the distortion due to the relief or the

viewing angle. An orthorectification process takes into account the conditions of data acquisition

(position of the satellite, specifications of the camera, etc.). In this exercise, first degree of

polynomial is used for the GC purpose.

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Table 1: Parameters of projection.

Polynomial 1 degree

Projection RSO

Spheriod name Everest ( Malaysia & Singapore 1984)

Datum name Everest ( Malaysia & Singapore 1984)

False easting 804671.2996 meters

False northing 0 meters

RSO type Malaysia

1.3.1.2 Resampling

Resampling is one approach commonly used to produce better estimates of the DN values

for individual pixels that have been reshifted to better fit their real spatial distributions. Thus, after

the various geometric corrections and translations have been applied, the net effect is that the

resulting redistribution of pixels involves their spatial displacements to new, more accurate relative

positions. However, the radiometric values of the displaced pixels no longer represent the real world

values that would be obtained if this new pixel array could be resensed by the scanner. The

particular mixture of surface objects or materials in the original pixel has changed somewhat

(depending on pixel size, number of classes and their proportions falling within the pixel, and extent

of continuation of these features in neighbouring pixels [a pond may fall within one or just a few

pixels; a forest can spread over many contiguous pixels]). There are 4 types of resampling, nearest

neighbour, bilinear interpolation, cubic convolution and bicubic spline.

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1.3.2 Atmospheric and Radiometric Correction

Radiometric correction of satellite imagery falls into two broad categories, absolute and

relative. Absolute radiometric correction converts the digital number of a pixel to a percentage

reflectance value using established transformation equations (Richter et al., 1990). Relative

radiometric correction normalizes multiple satellite scenes to each other. For both categories, the

majority of methodologies developed require ancillary data or the subjective selection of pseudo-

invariant features (PIFs) in the imagery. A more exhaustive comparison of radiometric correction

methods which require the use of ancillary data or ground targets can be found in Yuan and Elvidge

(1996), Yang and Lo (2000), and Song et al. (2001). Most methods of radiometric correction are not

applicable to all change detection studies and also require substantial time inputs and subjectivity on

the part of the image analyst.

1.3.2.1 Reasons for Radiometric and Atmospheric Correction

Analysis using uncorrected data assumes that the radiance of vegetation, soil, water and

other objects of interest have different reflectance characteristics for differentiation. Atmospheric

effects are not sufficiently great to affect their basic spectral separations. However, with extensive

and intensive applications of remotely sensed data in a variety of applications, atmospheric effects

become very important.

There are 6 reasons in support of radiometric and atmospheric correction for Landsat TM or

other remotely sensed data:

1. Multi-temporal TM data applications such as in lands use/cover change detection

2. Across scene (across path) comparison of spectral information of land cover types

3. Multi-sensor data applications such as multiple image mosaic to spatially produce a large

image, multi-sensor data integration such as TM and SPOT images

4. Quantitative analysis by combining old survey data with spectral data for applications such

as biomass estimation

5. Selected special applications such as using visible TM bands for mapping shoals and aquatic

plants beds

6. Band ratio operations such as vegetation indexes.

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There are several methods to apply in radiometric and atmospheric correction to eliminate the noise.

The methods are:

Histogram adjustment

Dark Object Subtraction (DOS) method

Cosine of Solar Zenith Angle Correction (COST) method referred to Chavez 1996

The method used in this task is Cosine of Solar Zenith Angle Correction (COST) method.

1.3.2.2 Cost Model

Introduction

The image-based COST method for atmospheric correction has been combined with radiometric

calibration in a model which streamlines this time-consuming but crucial processing step. Although

some initial scene-dependent calculations must be performed to obtain necessary model constants,

subsequent processing of raw DN values to atmospherically corrected reflectance values is

performed in a single process.( Chavez, 1996, S. M. Skirvin)

The model implements the Chavez (1996) improved dark-object atmospheric correction for Landsat

TM5 multispectral data (bands 1-5 and 7) and Landsat ETM+. The input image file is assumed to

have only these 6 bands.

Background

The inputs to the model are the Earth-Sun Distance, sun elevation angle, and minimum DN

values for each band.

The model first converts each minimum DN value to an at-satellite minimum spectral

radiance value:

For each band, the theoretical radiance of a dark object (assumed to have a reflectance of 1%

by Chavez 1996 and Moran et al. 1992) is computed:

spectral irradiance from Table 4 of Markham and Barker (1986), d is the sun-earth distance,

and theta is the solar zenith angle (90-sun elev).

A haze correction is computed using the computed dark object values (Chavez 1996):

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The fundamental radiance to reflectance (rho) equation (eq. 2 of Chavez 1996) is:

Otherwise, we can calculate reflectance value directly from DN value using the following

equation:

r = [- Lhazel + [((LMAXl – LMINl) / QCALMAX * DNmin)+ LMINl * ∏ * d2 ]] / ESUN *

[cos (∏ / 180 * (sun zenith angle))^2]

The formula and model can be download(http://arsc.arid.arizona.edu/resources/image_processing/landsat/ls5-atmo.html)

The figure below are used for modelling:

Landsat ETM

Figure 2: ETM Spectral Radiance Range and Solar Spectral Irradiances.

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Landsat TMhttp://hydrogis.geology.upatras.gr/res_net/data/L5TMCal2003.pdf

Figure 3: Landsat 5 Spectral Radiances

Figure 4: TM Solar Exoatmospheric Spectral Irradiances

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Landsat7 ETM date: 12/08/01

Landsat5 TM

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To calculate the sun earth distance, first we need to calculate the julian day based on the equation

given.

There are 2 ways to calculate the sun earth distance:

a) by using a formula

D= SE² : (sun to earth distance)²

: (1 – 0.01674 ( cos (0.9836*JD - 4)*Π/180))²

D= SE : √(d = se²)

Julian day : Date of the day refer to year.

LI% : 0.01* d² * cos²/ ∏ * ESUNλ

where ESUNλ = mean solar exo-atmospheric

d = the sun-earth distance

∏ = the solar zenith angle (90 – sun elevation)

Lλhaze: Lλmin - Lλ1%

b) take from the handbook

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Figure 5: Earth Sun Distance

Here, we can assume that the sun earth distance is 1 in astronomical units by average. And for this

model, I used to calculate the sun earth distance by formula.

1.3.3 Normalization

Multiple-date Image Normalization Using Regression

The multiple dates of remotely sensed data were acquired with varying sun angle,

atmospheric, and soil moisture conditions. Image normalization was performed to minimize pixel

DN variation caused by non-surface factors so that variations in pixel brightness value between

dates could be related to actual changes in surface conditions. Multiple-date image normalization

involves the selection of pseudo invariant features (targets) as radiometric tie. The coefficients and

intercept of the equation were used to compute a normalized image with the reference image.

Multiple-date image normalization uses histogram matching and regression modeling to

normalize one date of image into another so that the multiple dates of images have approximately

the same radiometric characteristics. When the atmospheric conditions are unknown, pseudo-

invariant ground targets may be used to normalize multi- temporal data sets to a single reference

scene. Multiple dates of remotely sensed data are required in land use/cover change detection.

Multiple image data are usually not captured on anniversary dates, thus sun angle, atmospheric

conditions and soil moisture likely vary from date to date.

Normalization can be used for minimizing the effects caused by the differences in sun angle,

atmospheric conditions and soil conditions. Regression models are usually used for image

normalization in multi-date image processing (Volchok and Schott 1986, Schott et al. 1988, Hall et

al. 1991, Muller 1993). One date of image acts as the reference image and another date of image as

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the predicted image. The regression equation is developed by correlating the values of the

normalization targets in both the image being normalized and the reference image, based on the

assumption that the values of the normalization targets are constant and any changes of these values

in other images are caused by the satellite sensor, atmospheric conditions and soil conditions. This

relationship can be described as: DNy=a+b *DNx, where the regression coefficient b is a

multiplicative component, which can correct for the difference in sun angle, atmosphere, etc. The

intercept a is an additive component, which can correct for the difference in atmospheric path

radiance between dates of image data. The key step in establishing the regression relationship is to

obtain the a and b parameters. This can be derived from a set of sample data selected from the

invariant objects such as an undisturbed dense mature forest, bare soil or a road in both dates of

images based on the assumption of similar environmental conditions. Olsson (1993) concluded that

multiple regression function from all TM bands were considerably better than the band-to-band

regression, especially for bands TM 1 through TM 3.

1.4 RESULT AND DISCUSSION

1.4.1 Geometric Correction

RMS error for this GC is 0.0105. The smallest the error, more accurate the image. The

GCP's must be well distributed on the image. The polynomial model is the most simple model and

can handle most of the georeferencing requirements. A first order polynomial transformation will

shift, scale and rotate your raster. It can be used to rectify scanned maps or satellite images on flat

areas.

For a first order polynomial model, a minimum of three ground control points is required to

calculate the model. In practice, it is better to collect more than three points. Given only three, if

one point is positionally wrong, it has much greater impact on the transformation. Thus, even

though the mathematical transformation error may increase as you collect more points, the overall

accuracy of the transformation will increase as well. So, in this GC, I have made about 49 GPS's.

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Figure 6: Ground Control Point

1.4.1.1 Resampling

From the resampling, showing that nearest neighbor is the best among 4 types of resamples. The

nearest neighbor algorithm simply assigns to each pixel the value of its nearest neighbor in the new

coordinate system. It is the fatest resampling technique and is appropriate for thematic data.

Figure 7: Nearest neighbour map.

From the resampling Landsat 5 TM image, showing that nearest neighbor is the best among 4 types

of resamples.

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Figure 8: Nearest neighbour image.

1.4.2 Atmospheric and Radiometric Correction

From the both result, showing that after applied the correction, the value of DN minimum

and the value of DN maximum is decrease. This result can be assume accepted because the value

for the radiance and reflectance image should not more than raw image after correction. The

reflectance value range are among 0 to 1.

Table 6: Raw data image of Landsat 7 ETM+ (12658_120801)

RAW DATA

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Table 7: Radiance and Reflectance Image of Landsat 7 ETM+ (12658_120801)

DN TO RADIANCE RADIANCE TO REFLECTANCE

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Table 8: Raw data image of Landsat 5 TM (12658_220404)

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RAW DATA

Table 9: Radiance and reflectance image of Landsat 5 TM (12658_220404)

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DN TO RADIANCE RADIANCE TO REFLECTANCE

1.5 CONCLUSION

Preprocessing is important procedure to eliminate noise present in the data and need to be

done before proceed to further processing. For geometric correction, GC points selected about 49

GCPs and RMSE are 0.0105. Well distributed GCP will increase the accuracy of the process. Image

preprocessing produces a corrected image that is as close as possible to true radiant energy and

spatial characteristics of the study area at the time of data collection. For radiometric correction, the

value of reflectance are about 0 – 1.

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