ge 113 remote sensing - bs geodetic engineering program · 2017. 3. 25. · lecture notes in ge...

Topic 6. Image Rectification and Restoration

Division of Geodetic Engineering College of Engineering and Information Technology Caraga State University

GE 113 – REMOTE SENSING

Lecturer: Engr. Jojene R. Santillan [email protected]

Lecture Notes in GE 113: Remote Sensing TOPIC 6. IMAGE RECTIFICATION AND RESTORATION

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Outline

• Part 1. Image Rectification and Restoration Concepts

• Part 2. Geometric Correction

• Part 3. Radiometric Correction

• Part 4. Noise Removal


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Expected Outcomes

• The students would be able to:

– Learn the concepts behind image rectification and restoration

– Identify the various computer-assisted procedures of image rectification and restoration

– Learn how to conduct the computer-assisted procedures through laboratory exercises

4 Lecture Notes in GE 113: Remote Sensing TOPIC 6. IMAGE RECTIFICATION AND RESTORATION

PART 1. IMAGE RECTIFICATION AND RESTORATION CONCEPTS


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Image Rectification and Restoration (1)

• Operations that aim to correct distorted or degraded image data to create a more faithful representation of the original scene.

• Often termed “Image pre-processing” operations/procedures: – They are normally done before

further manipulation and analysis of the image data are conducted to extract specific information

• Most of the distortions and degradations are caused by several factors during the image acquisition process


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• Typically involves initial processing of raw image data to: – Correct for geometric

distortions • i.e., to ensure that all pixels in the

image are correctly geo-referenced makes it possible to conduct accurate point, line and area measurements in the image

– Calibrate/correct the data radiometrically: • E.g., to convert DN to absolute

radiance values, to correct for atmospheric effects, to correct for changes in scene illumination

– Eliminate noise present in the data • e.g., to remove stripes, bit errors,

etc.

Image Rectification and Restoration (2)

“GEOMETRIC CORRECTION”

“RADIOMETRIC CORRECTION”

“NOISE REMOVAL”


PART 2. GEOMETRIC CORRECTION


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Geometric Correction

• Why it is needed?

– Raw digital images usually contain significant geometric distortions

– These distortions make the raw images unusable

• e.g., they cannot be used directly as a map base without subsequent processing.


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Example of a Distorted Landsat Image


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Example of a Distorted Landsat Image with Road Network


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Example of a Geometrically-corrected Landsat Image with Road Network


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Some Sources of Geometric Distortions

1. Variations in the altitude, attitude, and velocity of the sensor platform

2. Earth curvature

3. Earth’s eastward rotation

4. Atmospheric refraction

5. Relief displacement

By applying geometric correction procedures, the

distortions introduced by these factors are compensated so that the corrected image will have the highest practical geometric integrity.


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Distortions caused by variations in the altitude, attitude, and velocity of the sensor platform

From Lillesand et al., 2008


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Distortions caused by Earth’s curvature

• The Earth's curvature affects the geometric scale and exerts a type of panoramic effect

• Commonly observed in images acquired from high altitudes

• More pronounced at higher latitudes

• A non-systematic type of distortion

From: Gupta, R.P., 2013. Remote Sensing Geology, Springer Science & Business Media.


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Distortion caused by the Earth’s eastward rotation • The eastward rotation of

the Earth during a satellite orbit causes the sweep of scanning systems to cover an area slightly to the west of each previous scan.

• The resultant imagery is thus skewed across the image.

• This is known as skew distortion

• This distortion is systematic

• Common in imagery obtained from satellite multispectral scanners.



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Distortion caused by atmospheric refraction

• Serious error in location due to refraction can occur in images formed from energy detected at high altitudes or at acute angles


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Distortion caused by relief displacement • Relief displacement = displacement in the position of the

image of a ground object due to topographic variation (relief)

• Common phenomenon on all remote sensing data products, particularly those of high-relief terrain



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Relief Displacement Schematic



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Example image with relief displacement (1)


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Geometric Correction Process

• A 2-step procedure:

– Correction of systematic distortions (e.g., skew distortion)

– Correction of non-systematic/random/unpredictable distortions


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Correction of Systematic Distortions

• Easily corrected by applying formulas – Sources of distortions are

mathematically modeled

– Example: • Skew distortion due to

earth’s eastward rotation: – Corrected by deskewing

the imagery

» Involves offsetting each successive scan line slightly to the west

» The skewed-parallelogram appearance of satellite multispectral scanner data is a result of this correction



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Correction of Random Distortions (1)

• Corrected by analyzing well-distributed ground control points (GCPs) occurring in an image

– GCPs are features of known ground location that can be accurately located in an image


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• Correction Process: 1. Numerous GCPs are located both in terms of their two

image coordinates (column, row numbers) on the distorted image and in terms of their ground coordinates

2. These values are then submitted to a least squares regression analysis to determine the coefficients for two coordinate transformation equations that can be used to interrelate the geometrically correct (map) coordinates and the distorted-image coordinates

3. Once the coefficients for these equations are determined, the distorted image coordinates for any map position can be precisely estimated


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Mathematical Notation of the Transformation Equation

x = f1(X,Y) y = f2(X,Y)

Where

(x,y) = distorted-image coordinates (column, row)

(X,Y) = correct (map) coordinates

f1, f2 = transformation functions


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• Correction Process (continuation): 4. Using the transformation

equations, a process called resampling is used to determine the output (“corrected”) image matrix from the original (“distorted” image matrix:

• The coordinates of each element in

the undistorted output matrix are transformed to determine their corresponding location in the original input (distorted-image) matrix

• The intensity value or digital number assigned to a cell in the output matrix is determined based on the basis of the pixel values that surround its transformed position in the original input matrix

From Lillesand et al., 2008


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Resampling Methods

• Nearest neighbor – The DN of the

transformed (“corrected” pixel is equal to the DN of its closest (“original/distorted”) pixel

– Advantages: • simple to implement • Avoids the alteration of

the original pixel values

– Disadvantage: • Features in the output

image may be offset spatially by up to one-half pixel causes disjointed (“blocky”) appearance in the output image


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Resampling Methods

• Bilinear interpolation – The DN of the transformed (“corrected”) pixel is

equal to distance-weighted average of the 4 nearest pixels

– Advantage: • Smoother image appearance

– Disadvantage: • Alters the original DN values

Nearest neighbor Bilinear interpolation


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Resampling Methods

• Bicubic interpolation or cubic convolution – The DN of the transformed

(“corrected”) pixel is determined by evaluating the block of 16 pixels in the original image that surrounds the output pixel

– Advantage: • Smoother image appearance

• Slightly sharper image than the bilinear interpolation method

– Disadvantage: • Alters the original DN values


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Other Uses of Resampling Methods (aside from geometric correction of images)

• Used to overlay or register multiple dates of imagery (“image-to-image registration”)

• Used to register images of differing spatial resolution

• Used extensively to register image data and other sources of data in GISs.


PART 3. RADIOMETRIC CORRECTION


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Radiometric correction

• Why it is needed?

– The radiance measured by the sensor is influenced by different factors and it must be corrected

– Factors affecting radiance:

• Changes in scene illumination

• Atmopheric condition

• Viewing geometry

• Instrument response characteristics (e.g., how does a sensor records radiance as DNs?)


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Examples of Radiometric Corrections

• Sun elevation correction – Accounts for the

seasonal position of the sun relative to the earth

– Image data acquired under different illumination angles are normalized by calculating DN values assuming the sun was at the zenith on each data of sensing


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• Earth-sun distance correction

– Applied to normalize for the seasonal changes in the distance between the earth and the sun

Earth-sun distance = in Astronomical units

1 Astonomical unit = mean distance between the earth and the sun = 149.6 x 106 km

– The irradiance of the sun decreases as the square of the earth-sun distance


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• Combined sun elevation and earth-sun distance corrections:

Where: E = normalized solar irradiance E0 = solar irradiance at mean earth-sun distance θ0 = sun’s angle from the zenith d = earth-sun distance during the acquisition, in astronomical units


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• Atmospheric correction

– the large amounts of imagery collected by the satellites are largely contaminated by the effects of atmospheric particles through absorption and scattering of the radiation from the earth surface.

– The objective of atmospheric correction is to retrieve the surface reflectance (that characterizes the surface properties) from remotely sensed imagery by removing the atmospheric effects.

– Atmospheric correction has been shown to significantly improve the accuracy of image classification


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• Conversion of DNs to absolute radiance – DNs are

converted to spectral radiance using the sensor’s radiometric response function


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Example sensor-specific formula relating DN with Spectral Radiance


PART 4. NOISE REMOVAL


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Image Noise

• Any unwanted disturbance in image data that is due to limitations in the sensing, signal digitization, or data recording process

• Noise can either degrade or totally mask the true radiometric information content of a digital image


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Potential Sources of Noise

• Periodic drift or malfunction of a detector

• Electronic interference between sensor components

• “Hiccups” in the data transmission and recording sequence


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Example of Noise in Image

• Stripes or bands – Appearance of defective lines (e.g., in Landsat MSS

data) due to the variations in the response of individual detectors • Results to relatively higher or lower values along every sixt

line

• Line drop – A number of adjacent pixels along a line may contain

spurious DNs – Caused by data transmission errors

• Bit errors or shot noise – Random noise in the image – “spikey” in characterer – Causes images to have a “salt and pepoper” or “snowy

appearance”


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Example of Striped Image


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Example of Image with Line Drops


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Example of Image with Bit Errors


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Noise Removal Process

• Usually precedes any subsequent enhancement or classification of the image data

• Correction/removal depends on the nature of noise:

– Systematic (periodic)

– Random

– Combination of systematic and random noise

• Noise removal are done through:

– Interpolation of the DN values

– Application of moving window algorithms


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• Questions or clarifications?


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References/Further Reading

• Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2008). Remote Sensing and Image Interpretation 6th Edition. United States of America: John Wiley & Sons, Inc.

• Online Tutorial: Fundamentals of Remote Sensing – “Pre-processing”. Available at http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9403
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ge 113 remote sensing - bs geodetic engineering program · 2017. 3. 25. · lecture notes in ge...

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