Intro of digital image processing

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Intro of digital image processing

• Lecture 5a

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Remote Sensing Raster (Matrix) Data Format
Digital number of column 5, row 4 at band 2 is
expressed as BV5,4,2 105.
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Image file formats

• BSQ (Band Sequential Format)
• each line of the data followed immediately by the
  next line in the same spectral band. This format
  is optimal for spatial (X, Y) access of any part
  of a single spectral band. Good for multispectral
  images
• BIP (Band Interleaved by Pixel Format)
• the first pixel for all bands in sequential
  order, followed by the second pixel for all
  bands, followed by the third pixel for all bands,
  etc., interleaved up to the number of pixels.
  This format provides optimum performance for
  spectral (Z) access of the image data. Good for
  hyperspectral images
• BIL (Band Interleaved by Line Format)
• the first line of the first band followed by the
  first line of the second band, followed by the
  first line of the third band, interleaved up to
  the number of bands. Subsequent lines for each
  band are interleaved in similar fashion. This
  format provides a compromise in performance
  between spatial and spectral processing and is
  the recommended file format for most ENVI
  processing tasks. Good for images with 20-60
  bands

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Band 2
Band 3
Band 4
Matrix notation for band 2
BIL
BSQ
BIP
5

• Band sequential (BSQ) format stores information
  for the image one band at a time. In other words,
  data for all pixels for band 1 is stored first,
  then data for all pixels for band 2, and so on.
• Valueimage(c, r, b)
• Band interleaved by pixel (BIP) data is similar
  to BIL data, except that the data for each pixel
  is written band by band. For example, with the
  same three-band image, the data for bands 1, 2
  and 3 are written for the first pixel in column
  1 the data for bands 1, 2 and 3 are written for
  the first pixel in column 2 and so on.
• Valueimage(b, c, r)
• Band interleaved by line (BIL) data stores pixel
  information band by band for each line, or row,
  of the image. For example, given a three-band
  image, all three bands of data are written for
  row 1, all three bands of data are written for
  row 2, and so on, until the total number of rows
  in the image is reached.
• Valueimage(c, b, r)

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What is image processing

• Is enhancing an image or extracting information
  or features from an image
• Computerized routines for information extraction
  (eg, pattern recognition, classification) from
  remotely sensed images to obtain categories of
  information about specific features.
• Many more

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Image Processing Includes

• Image quality and statistical evaluation
• Radiometric correction
• Geometric correction
• Image enhancement and sharpening
• Image classification
• Pixel based
• Object-oriented based
• Accuracy assessment of classification
• Post-classification and GIS
• Change detection

GEO5083 Remote Sensing Image Processing and
Analysis, spring 2012
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Image Quality
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• Many remote sensing datasets contain
  high-quality, accurate data. Unfortunately,
  sometimes error (or noise) is introduced into the
  remote sensor data by
• the environment (e.g., atmospheric scattering,
  cloud),
• random or systematic malfunction of the remote
  sensing system (e.g., an uncalibrated detector
  creates striping), or
• improper pre-processing of the remote sensor
  data prior to actual data analysis (e.g.,
  inaccurate analog-to-digital conversion).

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154
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Cloud
155
160
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MODIS True 143
163
164
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Clouds in ETM
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Striping Noise and Removal
CPCA
Combined Principle Component Analysis
Xie et al. 2004
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Speckle Noise and Removal
Blurred objects and boundary
G-MAP
Gamma Maximum A Posteriori Filter
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Univariate descriptive image statistics

• The mode is the value that occurs most frequently
  in a distribution and is usually the highest
  point on the curve (histogram). It is common,
  however, to encounter more than one mode in a
  remote sensing dataset.
• The median is the value midway in the frequency
  distribution. One-half of the area below the
  distribution curve is to the right of the median,
  and one-half is to the left
• The mean is the arithmetic average and is defined
  as the sum of all brightness value observations
  divided by the number of observations.

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Cont

• Min
• Max
• Variance
• Standard deviation
• Coefficient of variation (CV)
• Skewness
• Kurtosis
• Moment

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Multivariate Image Statistics

• Remote sensing research is often concerned with
  the measurement of how much radiant flux is
  reflected or emitted from an object in more than
  one band. It is useful to compute multivariate
  statistical measures such as covariance and
  correlation among the several bands to determine
  how the measurements covary. Variancecovariance
  and correlation matrices are used in remote
  sensing principal components analysis (PCA),
  feature selection, classification and accuracy
  assessment.

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Covariance

• The different remote-sensing-derived spectral
  measurements for each pixel often change together
  in some predictable fashion. If there is no
  relationship between the brightness value in one
  band and that of another for a given pixel, the
  values are mutually independent that is, an
  increase or decrease in one bands brightness
  value is not accompanied by a predictable change
  in another bands brightness value. Because
  spectral measurements of individual pixels may
  not be independent, some measure of their mutual
  interaction is needed. This measure, called the
  covariance, is the joint variation of two
  variables about their common mean.

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Correlation
To estimate the degree of interrelation between
variables in a manner not influenced by
measurement units, the correlation coefficient,
is commonly used. The correlation between two
bands of remotely sensed data, rkl, is the ratio
of their covariance (covkl) to the product of
their standard deviations (sksl) thus
If we square the correlation coefficient (rkl),
we obtain the sample coefficient of determination
(r2), which expresses the proportion of the total
variation in the values of band l that can be
accounted for or explained by a linear
relationship with the values of the random
variable band k. Thus a correlation coefficient
(rkl) of 0.70 results in an r2 value of 0.49,
meaning that 49 of the total variation of the
values of band l in the sample is accounted for
by a linear relationship with values of band k.
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example
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Univariate statistics
covariance
Correlation coefficient
Covariance
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Types of radiometric correction
2

• Detector error or sensor error (internal error)
• Atmospheric error (external error)
• Topographic error (external error)

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Atmospheric correction

• There are several ways to atmospherically correct
  remotely sensed data. Some are relatively
  straightforward while others are complex, being
  founded on physical principles and requiring a
  significant amount of information to function
  properly. This discussion will focus on two major
  types of atmospheric correction
• Absolute atmospheric correction, and
• Relative atmospheric correction.

60 miles or 100km
Scattering, Absorption Refraction, Reflection
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Absolute atmospheric correction

• Solar radiation is largely unaffected as it
  travels through the vacuum of space. When it
  interacts with the Earths atmosphere, however,
  it is selectively scattered and absorbed. The sum
  of these two forms of energy loss is called
  atmospheric attenuation. Atmospheric attenuation
  may 1) make it difficult to relate hand-held in
  situ spectroradiometer measurements with remote
  measurements, 2) make it difficult to extend
  spectral signatures through space and time, and
  (3) have an impact on classification accuracy
  within a scene if atmospheric attenuation varies
  significantly throughout the image.
• The general goal of absolute radiometric
  correction is to turn the digital brightness
  values (or DN) recorded by a remote sensing
  system into scaled surface reflectance values.
  These values can then be compared or used in
  conjunction with scaled surface reflectance
  values obtained anywhere else on the planet.

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a) Image containing substantial haze prior to
atmospheric correction. b) Image after
atmospheric correction using ATCOR (Courtesy
Leica Geosystems and DLR, the German Aerospace
Centre).
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relative radiometric correction

• When required data is not available for absolute
  radiometric correction, we can do relative
  radiometric correction
• Relative radiometric correction may be used to
• Single-image normalization using histogram
  adjustment
• Multiple-data image normalization using
  regression

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Single-image normalization using histogram
adjustment

• The method is based on the fact that infrared
  data (gt0.7 ?m) is free of atmospheric scattering
  effects, whereas the visible region (0.4-0.7 ?m)
  is strongly influenced by them.
• Use Dark Subtract to apply atmospheric scattering
  corrections to the image data. The digital number
  to subtract from each band can be either the band
  minimum, an average based upon a user defined
  region of interest, or a specific value

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Dark Subtract using band minimum
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Topographic correction

• Topographic slope and aspect also introduce
  radiometric distortion (for example, areas in
  shadow)
• The goal of a slope-aspect correction is to
  remove topographically induced illumination
  variation so that two objects having the same
  reflectance properties show the same brightness
  value (or DN) in the image despite their
  different orientation to the Suns position
• Based on DEM, sun-elevation

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Conceptions of geometric correction
3

• Geocoding geographical referencing
• Registration geographically or nongeographically
  (no coordination system)
• Image to Map (or Ground Geocorrection)
• The correction of digital images to ground
  coordinates using ground control points collected
  from maps (Topographic map, DLG) or ground GPS
  points.
• Image to Image Geocorrection
• Image to Image correction involves matching the
  coordinate systems or column and row systems of
  two digital images with one image acting as a
  reference image and the other as the image to be
  rectified.
• Spatial interpolation from input position to
  output position or coordinates.
• RST (rotation, scale, and transformation),
  Polynomial, Triangulation
• Root Mean Square Error (RMS) The RMS is the
  error term used to determine the accuracy of the
  transformation from one system to another. It is
  the difference between the desired output
  coordinate for a GCP and the actual.
• Intensity (or pixel value) interpolation (also
  called resampling) The process of extrapolating
  data values to a new grid, and is the step in
  rectifying an image that calculates pixel values
  for the rectified grid from the original data
  grid.
• Nearest neighbor, Bilinear, Cubic

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Image enhancement
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• image reduction,
• image magnification,
• transect extraction,
• contrast adjustments (linear and non-linear),
• band ratioing,
• spatial filtering,
• fourier transformations,
• principle components analysis,
• texture transformations, and
• image sharpening

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Purposes of image classification
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• Land use and land cover (LULC)
• Vegetation types
• Geologic terrains
• Mineral exploration
• Alteration mapping
• .

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What is image classification or pattern
recognition

• Is a process of classifying multispectral
  (hyperspectral) images into patterns of varying
  gray or assigned colors that represent either
• clusters of statistically different sets of
  multiband data, some of which can be correlated
  with separable classes/features/materials. This
  is the result of Unsupervised Classification, or
• numerical discriminators composed of these sets
  of data that have been grouped and specified by
  associating each with a particular class, etc.
  whose identity is known independently and which
  has representative areas (training sites) within
  the image where that class is located. This is
  the result of Supervised Classification.
• Spectral classes are those that are inherent in
  the remote sensor data and must be identified and
  then labeled by the analyst.
• Information classes are those that human beings
  define.

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supervised classification. Identify known a
priori through a combination of fieldwork, map
analysis, and personal experience as training
sites the spectral characteristics of these
sites are used to train the classification
algorithm for eventual land-cover mapping of the
remainder of the image. Every pixel both within
and outside the training sites is then evaluated
and assigned to the class of which it has the
highest likelihood of being a member.
unsupervised classification, The computer or
algorithm automatically group pixels with similar
spectral characteristics (means, standard
deviations, covariance matrices, correlation
matrices, etc.) into unique clusters according to
some statistically determined criteria. The
analyst then re-labels and combines the spectral
clusters into information classes.
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Hard vs. Fuzzy classification

• Supervised and unsupervised classification
  algorithms typically use hard classification
  logic to produce a classification map that
  consists of hard, discrete categories (e.g.,
  forest, agriculture).
• Conversely, it is also possible to use fuzzy set
  classification logic, which takes into account
  the heterogeneous and imprecise nature (mix
  pixels) of the real world. Proportion of the m
  classes within a pixel (e.g., 10 bare soil, 10
  shrub, 80 forest). Fuzzy classification schemes
  are not currently standardized.

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Pixel-based vs. Object-oriented classification

• In the past, most digital image classification
  was based on processing the entire scene pixel by
  pixel. This is commonly referred to as per-pixel
  (pixel-based) classification.
• Object-oriented classification techniques allow
  the analyst to decompose the scene into many
  relatively homogenous image objects (referred to
  as patches or segments) using a multi-resolution
  image segmentation process. The various
  statistical characteristics of these homogeneous
  image objects in the scene are then subjected to
  traditional statistical or fuzzy logic
  classification. Object-oriented classification
  based on image segmentation is often used for the
  analysis of high-spatial-resolution imagery
  (e.g., 1 ? 1 m Space Imaging IKONOS and
  0.61 ? 0.61 m Digital Globe QuickBird).

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Unsupervised classification

• Uses statistical techniques to group
  n-dimensional data into their natural spectral
  clusters, and uses the iterative procedures
• label certain clusters as specific information
  classes
• K-mean and ISODATA
• For the first iteration arbitrary starting values
  (i.e., the cluster properties) have to be
  selected. These initial values can influence the
  outcome of the classification.
• In general, both methods assign first arbitrary
  initial cluster values. The second step
  classifies each pixel to the closest cluster. In
  the third step the new cluster mean vectors are
  calculated based on all the pixels in one
  cluster. The second and third steps are repeated
  until the "change" between the iteration is
  small. The "change" can be defined in several
  different ways, either by measuring the distances
  of the mean cluster vector have changed from one
  iteration to another or by the percentage of
  pixels that have changed between iterations.
• The ISODATA algorithm has some further
  refinements by splitting and merging of clusters.
  Clusters are merged if either the number of
  members (pixel) in a cluster is less than a
  certain threshold or if the centers of two
  clusters are closer than a certain threshold.
  Clusters are split into two different clusters if
  the cluster standard deviation exceeds a
  predefined value and the number of members
  (pixels) is twice the threshold for the minimum
  number of members.

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Supervised classificationtraining sites
selection

• Based on known a priori through a combination of
  fieldwork, map analysis, and personal experience
• on-screen selection of polygonal training data
  (ROI), and/or
• on-screen seeding of training data (ENVI does
  not have this, Erdas Imagine does).
• The seed program begins at a single x, y location
  and evaluates neighboring pixel values in all
  bands of interest. Using criteria specified by
  the analyst, the seed algorithm expands outward
  like an amoeba as long as it finds pixels with
  spectral characteristics similar to the original
  seed pixel. This is a very effective way of
  collecting homogeneous training information.
• From spectral library of field measurements

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Selecting ROIs
Alfalfa
Cotton
Grass
Fallow
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Supervised classification methods

• Various supervised classification algorithms may
  be used to assign an unknown pixel to one of m
  possible classes. The choice of a particular
  classifier or decision rule depends on the nature
  of the input data and the desired output.
  Parametric classification algorithms assumes that
  the observed measurement vectors Xc obtained for
  each class in each spectral band during the
  training phase of the supervised classification
  are Gaussian that is, they are normally
  distributed. Nonparametric classification
  algorithms make no such assumption.
• Several widely adopted nonparametric
  classification algorithms include
• one-dimensional density slicing
• parallepiped,
• minimum distance,
• nearest-neighbor, and
• neural network and expert system analysis.
• The most widely adopted parametric classification
  algorithms is the
• maximum likelihood.
• Hyperspectral classification methods
• Binary Encoding
• Spectral Angle Mapper
• Matched Filtering
• Spectral Feature Fitting
• Linear Spectral Unmixing

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Supervised classification method Spectral
Feature Fitting
Source http//popo.jpl.nasa
.gov/html/data.html
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Accuracy assessment of classification
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• Remote sensing-derived thematic information are
  becoming increasingly important. Unfortunately,
  they contain errors.
• Errors come from 5 sources
• Geometric error still there
• None of atmospheric correction is perfect
• Clusters incorrectly labeled after unsupervised
  classification
• Training sites incorrectly labeled before
  supervised classification
• None of classification method is perfect
• We should identify the sources of the error,
  minimize it, do accuracy assessment, create
  metadata before being used in scientific
  investigations and policy decisions.
• We usually need GIS layers to assist our
  classification.

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Post-classification and GIS
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salt- and- pepper
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types

• Majority/Minority Analysis
• Clump Classes
• Morphology Filters
• Sieve Classes
• Combine Classes
• Classification to vector (GIS)

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Change detection
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• Change detect involves the use of multi-temporal
  datasets to discriminate areas of land cover
  change between dates of imaging.
• Ideally, it requires
• Same or similar sensor, resolution, viewing
  geometry, spectral bands, radiomatric resolution,
  acquisition time of data, and anniversary dates
• Accurate spatial registration (less than 0.5
  pixel error)
• Methods
• Independently classified and registered, then
  compare them
• Classification of combined multi-temporal
  datasets,
• Principal components analysis of combined
  multi-temporal datasets
• Image differencing (subtracting), (needs to find
  change/no change threshold, change area will be
  in the tails of the histogram distribution)
• Image ratioing (dividing), (needs to find
  change/no change threshold, change area will be
  in the tails of the histogram distribution)
• Change vector analysis
• Delta transformation

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Example stages of development
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Sun City Hilton Head
1994
1996
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1974 1,040 urban hectares 1994 3,263
urban hectares 315 increase