REMOTE SENSING:REMOTE SENSING: Lecture Ch. 11...

REMOTE SENSING:REMOTE SENSING:Lecture Ch. 11

PREPROCESSINGPREPROCESSING

D t f El t i l E i iDept. of Electrical EngineeringFaculty of EngineeringUniversity of Indonesia

Lecture in PPT DoEE (c) 2008

AgendaAgenda

• IntroductionIntroduction• Feature Extraction• Radiometric PreprocessingRadiometric Preprocessing• Geometric Preprocessing• Map Projection• Map Projection• Data Fusion• Conclusion• Conclusion


IntroductionIntroduction• Preprocessing is a preparatory phase (in image p g p p y p ( g

processing) to improve image quality from undesirable of atmospheric interference, system noise sensor motion etcnoise, sensor motion, etc.

• Preprocessing operations are:– Feature ExtractionFeature Extraction– Radiometric preprocessing– Geometric preprocessing

• Some images with good quality may not require the preprocessing.


Feature ExtractionFeature Extraction

• Feature extraction is a technique to get the “essential q gelements” of an image.

• It may increase accuracy and reduce the number of spectral channels or bands that must be analyzed,spectral channels or bands that must be analyzed, thereby reducing computational demands. It also increases speed and reduce costs of analysis.

• A variance covariance matrix shows interrelationships• A variance-covariance matrix shows interrelationships between pairs of bands; some pairs show rather strong correlation. High correlation between pairs of bands (about 0 9) means that the values in two channels are(about 0.9) means that the values in two channels are closely related.



• A more powerful approach to feature selection applies a p pp ppmethod of data analysis called principal components analysis (PCA).

• PCA identifies the optimum linear combinations of thePCA identifies the optimum linear combinations of the original channels that can account for variation of pixel values within an image. Linear combinations are of the form:form:

• Where X1, X2, X3, and X4 are the values in four spectral h l d C1 C2 C3 d C4 ffi i t

44332211 XCXCXCXCA +++=

channels, and C1, C2, C3, and C4 are coefficients applied individually to the values in the respective channels.


Feature ExtractionFeature Extraction• Optimum values for coefficients are calculated by a

d th t th t th l th dprocedure that ensures that the values they produce account for maximum variation within the entire data set.

• It set of coefficients provides the maximum information pthat can be conveyed by any single channel formed by a linear combination of the original channels.

• The effectiveness of this procedure depends uponThe effectiveness of this procedure depends upon calculation of the optimum coefficients.

• PCA transformation applies only to specific image at hand and each new image requires a new calculation ofhand, and each new image requires a new calculation of the PCA.

• For some applications, this constraint limits the ff ti f th t h i


effectiveness of the technique


Anal e

93%

Analyze

7%

Discard!


SubsetSubset

• Subsets is aimed to minimize computer storage, and the p ganalyst’s time and effort portions of larger images selected to show only the region of interest.

• Often subsets must be “registered” (matched) to otherOften subsets must be registered (matched) to other data, or to other projects.

• Since time and computational effort devoted to matching images to maps or other images increase with largeimages to maps or other images increase with large images, It may useful to prepare a preliminary subset (large enough to conduct the image registration effectively) before selecting the final smaller subset foreffectively) before selecting the final, smaller, subset for analytical use.


SubsetSubset


Radiometric PreprocessingRadiometric Preprocessing

• Any sensor that observes the earth’s surfaceAny sensor that observes the earth s surface using visible or near visible radiation will record a mixture of two kinds of brightness's. One brightness is due to the reflectance from the Earth’s surface and the brightness of the

t h it lfatmosphere itself.• Radiometric preprocessing influences the

brightness values of an image to correct forbrightness values of an image to correct for sensor malfunctions or to adjust the values to compensate for atmospheric degradation


compensate for atmospheric degradation.


• Preprocessing operations to correct for atmospheric p g p pdegradation fall into three rather broad categories:– Based upon efforts to model the physical behavior of

the radiation as it passes through the atmosphere. It’sthe radiation as it passes through the atmosphere. It s very complex and usually requiring detailed data and intricate computer program.Based upon examination of reflectance from objects– Based upon examination of reflectance from objects of known or assumed brightness recorded by multispectral imagery.U i Hi t Mi i M th d (HMM) th D k– Using Histogram Minimum Method (HMM) or the Dark Object Subtraction (DOS) technique by subtracting the brightness values contributed by atmosphere.



• HMM has the advantage of simplicity, directness, and g p yalmost universal applicability, as it exploits information present within the image itself.

• Whereas the HMM procedure is applied to entire scenes,Whereas the HMM procedure is applied to entire scenes, or to very large areas, the regression technique can be applied to local areas (or possibly only 100-500 pixels each) assuring that the adjustment is tailored toeach), assuring that the adjustment is tailored to condition important with in specific regions.

• An extension of the regression technique is to examine the variance covariance matrix The set of variance andthe variance-covariance matrix. The set of variance and covariance between all band pairs on data. It is called as Covariance Matrix Method (CMM).


DestripingDestriping

• Destriping refers to application of algorithms to adjust p g pp g jincorrect brightness values to values thought to be near the correct values.

• The strategies for destriping:The strategies for destriping:– By replacing bad pixels with values base upon the average of

adjacent pixel not influenced by striping.– By replacing bad pixels with new values based upon the meanBy replacing bad pixels with new values based upon the mean

and standard deviation of the band in question, or upon statistics developed for each detector.

– By combining elements of both strategies or using histogram normalization. This procedure attempts to bring all values in a band to a normalized mean and variance, based on overall statistics for the entire band


DestripingDestriping


Image MatchingImage Matching

• Image matching is the process of superimposing two g g p p p gimages of the same area, then moving them to find the position at which they best match.

• One of the simplest and most widely used strategies is toOne of the simplest and most widely used strategies is to digitally overlay the two images, then calculate a correlation for the area where the two images overlap.

• Matched positions are systematically shifted pixel by• Matched positions are systematically shifted, pixel by pixel, until all possible matches have been attempted.

• At each position a new correlation value is calculated, th d Th ti iti i th iti th tthen saved. The optimum position is the position that yields the highest correlations.


Geometric PreprocessingGeometric Preprocessing

• The images are treated simply as an array of values that g p y ymust be manipulated to create another array with the desired geometry.

• The locations of the output pixels are derived fromThe locations of the output pixels are derived from location information provided by ground control points(GCP), location on the input image that can be located with precision on the ground and on the correct mapwith precision on the ground and on the correct map.

• The strategies to estimate the values of pixels in the corrected image:

N t i hb li– Nearest neighbor resampling– Bilinear interpolation– Cubic convolution



Resampling Nearest neighbor resampling


Resampling Nearest neighbor resampling


Conceptual diagram showing nearest neighbor resampling method



Nearest Neighbor - Uses the input cell value closest to the output cell as the assigned value to the output cell


the output cell as the assigned value to the output cell


Bilinear interpolation Cubic convolution


p


Bilinear Interpolation - Calculates the output cell value by calculating the weighted average of the four closest

input cells (a 2x2 array) based on distance


input cells (a 2x2 array) based on distance.


Cubic Convolution - Calculates the output cell value by calculating the weighted average of the closest 16

input cells (a 4x4 array) based on distance


input cells (a 4x4 array) based on distance.

Ground Control PointsGround Control Points• Ground Control Points (GCP) are features that can be

l t d ith i i d tlocated with precision and accuracy on accurate maps yet are also easily located on digital images.

• Ideally, GCP could be as small as a single pixel, if one ld b il id tifi d i t it b k dcould be easily identified against its background.

• The registration error decreases as the number of GCP is increased. Obviously, it is better to have more rather than fewer GCPthan fewer GCP.

• But, the quality of GCP accuracy may decrease as their number increases, because the analyst usually picks the best points firstbest points first.

• The 16 GCP may be a reasonable number if each can be located with an accuracy of one-third of a pixel. But it may not be sufficient if the GPS are poorly distributed


may not be sufficient if the GPS are poorly distributed.

Ground Control PointsGround Control Points

Selection of distinctive ground control points (GCP)


Ground Control PointsGround Control Points


Map ProjectionMap Projection

• A map projection is a system of transformations that p p j yenables locations on the spherical Earth to be represented systematically on a flat map.

• The Mercator projection was the first projection to attainThe Mercator projection was the first projection to attain widespread use, because of its utility for marine navigation.

• The Mercator projection can be envisioned as a• The Mercator projection can be envisioned as a transformation of the network of lines of latitude and longitude (known as the graticule) onto a flat surface such that the meridians of longitude form equally spacedsuch that the meridians of longitude form equally spaced vertical lines and the parallels of latitude form horizontal lines intersecting the meridians at the right angles.



Th L b t li d i l l j tiThe Lambert cylindrical equal-area projection as an example of an equivalent, cylindrical projection



The Plate Carree projection as an example of an idi t t li d i l j ti


equidistant, cylindrical projection


The Mercator as an example of a conformal, cylindrical


projection

Data FusionData Fusion

• Data fusion refers to process that bring images of varied p g gresolution into a single image that incorporates.

• Image fusion involves the merging of a multispectral image of relatively coarse spatial resolution with anotherimage of relatively coarse spatial resolution with another image of the same region acquired at finer spatial resolution.

• Spectral domain procedures project the multispectral• Spectral domain procedures project the multispectral bands into spectral data space, the find the new (transformed) band most closely correlated with the panchromatic imagepanchromatic image.

• The intensity-hue-saturation (IHS) technique is one of the spectral domain procedure


Data FusionData Fusion

• HIS refers to the three dimensions of multispectral pdata. Intensity is equivalent to brightness, hue refers to dominant wavelength, and saturation specifies purity the degree to which a specific color ispurity, the degree to which a specific color is dominated by a single wavelength.

• Spatial domain procedures extract the high-f i ti f fi l ti i dfrequency variation of a fine-resolution image and then insert it into the multispectral framework of a corresponding coarse-resolution image.p g g

• The high-pass filter (HPF) technique is an example.


ConclusionConclusion

• Preprocessing is a preparatory operations inPreprocessing is a preparatory operations in image processing to improve image quality as the basis for later analyses.

• Preprocessing includes a wide range operations, they are feature extraction, radiometric correction, geometric correction, etc.

• A map projection is required to transform the bl l i h h i l E h benables locations on the spherical Earth to be

represented systematically on a flat map.


ReferencesReferences• James B. Campbell. Introduction to Remote p

Sensing -fourth edition, The Guilford press, New York, 2007.D i Whi Di l f i l l d li ti• Denis White. Display of pixel loss and replication in reprojecting raster data from the sinusoidal projection 2006projection, 2006.

• R. Douglas Ramsey. Introductory Digital Image Processing.

• http://gisremote.blogspot.com. GIS and Remote Sensing.


REMOTE SENSING:REMOTE SENSING: Lecture Ch. 11...

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