Image Enhancement

1
Chapter

• Image Enhancement
• Analysis and applications of remote sensing
  imagery
• Instructor Dr. Cheng-Chien Liu
• Department of Earth Sciences
• National Cheng Kung University
• Last updated 5 June 2015

2
Introduction

• Image enhancement
• Mind ? excellent interpreter
• Eye ? poor discriminator
• Computer ? amplify the slight differences to make
  them readily observable
• Categorization of image enhancement
• Point operation
• Local operation
• Order
• Restoration ? noise removal ? enhancement

3
Contrast manipulation

• Gray-level thresholding
• Segment
• Fig 7.11
• (a) TM1
• (b) TM4
• (c) TM4 histogram
• (d) TM1 brightness variation in water areas only
• Level-slicing
• Divided into a series of analyst-specified slices
• Fig 7.12

4
Contrast manipulation (cont.)

• Contrast stretching
• Accentuate the contrast between features of
  interest
• Fig 7.13
• (a) Original histogram
• (b) No stretch
• (c) Linear stretch
• Fig 7.14 linear stretch algorithm, look-up table
  (LUT) procedure
• (d) Histogram-equalized stretch
• (e) Special stretch
• Fig 7.15 Effect of contrast stretching
• (a) Features of similar brightness are virtually
  indistinguishable
• (b) Stretch that enhances contrast in bright
  image areas
• (c) Stretch that enhances contrast in dark image
  areas
• Non-linear stretching sinusoidal, exponential,
• Monochromatic ? color composite

5
Spatial feature manipulation

• Spatial filtering
• Spectral filter ? Spatial filter
• Spatial frequency
• Roughness of the tonal variations occurring in an
  image
• High ? rough
• e.g. across roads or field borders
• Low ? smooth
• e.g. large agricultural fields or water bodies
• Spatial filter ? local operation
• Low pass filter (Fig 7.16b)
• Passing a moving window throughout the original
  image
• High pass filter (Fig 7.16c)
• Subtract a low pass filtered image from the
  original, unprocessed image

6
Spatial feature manipulation (cont.)

• Convolution
• The generic image processing operation
• Spatial filter ? convolution
• Procedure
• Establish a moving window (operators/kernels)
• Moving the window throughout the original image
• Example
• Fig 7.17
• (a) Kernel
• Size odd number of pixels (3x3, 5x5, 7x7, )
• Can have different weighting scheme (Gaussian
  distribution, )
• (b) original image DN
• (c) convolved image DN
• Pixels around border cannot be convolved

7
Spatial feature manipulation (cont.)

• Edge enhancement
• Typical procedures
• Roughness ? kernel size
• Rough ? small
• Smooth ? large
• Add back a fraction of gray level to the high
  frequency component image
• High frequency ? exaggerate local contrast but
  lose low frequency brightness information
• Contrast stretching
• Directional first differencing
• Determine the first derivative of gray levels
  with respect to a given direction
• Normally add the display value median back to
  keep all positive values
• Contrast stretching
• Example
• Fig 7.20a original image
• Fig 7.20b horizontal first difference image
• Fig 7.20c vertical first difference image
• Fig 7.20d diagonal first difference image
• Fig 7.21 cross-diagonal first difference image ?
  highlight all edges

8
Spatial feature manipulation (cont.)

• Fourier analysis
• Spatial domain ? frequency domain
• Fourier transform
• Quantitative description
• Conceptual description
• Fit a continuous function through the discrete DN
  values if they were plotted along each row and
  column in an image
• The peaks and valleys along any given row or
  column can be described mathematically by a
  combination of sine and cosine waves with various
  amplitudes, frequencies, and phases
• Fourier spectrum
• Fig 7.22
• Low frequency ? center
• High frequency ? outward
• Vertical aligned features ? horizontal components
• Horizontal aligned features ? vertical components

9
Spatial feature manipulation (cont.)

• Fourier analysis (cont.)
• Inverse Fourier transform
• Spatial filtering (Fig 7.23)
• Noise elimination (Fig 7.24)
• Noise pattern ? vertical band of frequencies ?
  wedge block filter
• Summary
• Most image processing ? spatial domain
• Frequency domain (e.g. Fourier transform) ?
  complicate and computational expensive

10
Multi-image manipulation

• Spectral ratioing
• DNi / DNj
• Advantage
• Convey the spectral or color characteristics of
  image features, regardless of variations in scene
  illumination conditions
• Fig 7.25
• deciduous trees ? coniferous trees
• Sunlit side ? shadowed side
• Example NIR/Red ? stressed and nonstressed
  vegetation ? quantify relative vegetation
  greenness and biomass
• Number of ratio combination Cn2
• Landsat MSS 12
• Landsat TM or ETM 30

11
Multi-image manipulation (cont.)

• Spectral ratioing (cont.)
• Fig 7.26 ratioed images derived from Landsat TM
  data
• (a) TM1/TM2 highly correlated ? low contrast
• (b) TM3/TM4
• Red road?, water? ? lighter tone
• NIR vegetation? ? darker tone
• (c) TM5/TM2
• Green and MIR vegetation? ? lighter tone
• But some vegetation looks dark ? discriminate
  vegetation type
• (d) TM3/TM7
• Red road?, water? ? lighter tone
• MIR low but varies with water turbidity ? water
  turbidity
• False color composites ? twofold advantage
• Too many combination ? difficult to choose
• Landsat MSS C(4, 2)/2 6, C(6, 3) 20
• Landsat TM C(6, 2)/2 15, C(15, 3) 455
• Optimum index factor (OIF)
• Variance? correlation ?? OIF?
• Best OIF for conveying the overall information in
  a scene may not be the best OIF for conveying the
  specific information ? need some trial and error

12
Multi-image manipulation (cont.)

• Spectral ratioing (cont.)
• Intensity blind ? troublesome
• Hybrid color ratio composite one ratio another
  band
• Noise removal is an important prelude
• Spectral ratioing enhances noise patterns
• Avoid mathematically blow up the ratio
• DN? R arctan(DNx/DNy)
• arctan ranges from 0 to 1.571. Typical value of R
  is chosen to be 162.3 ? DN?ranges from 0 to 255

13
Multi-image manipulation (cont.)

• Principal and canonical components
• Two techniques
• Reduce redundancy in multispectral data
• Extensive interband correlation problem (Fig
  7.49)
• Prior to visual interpretation or classification
• Example Fig 7.27
• DNI a11DNA a12DNB DNII a21DNA a22DNB
• Eigenvectors (principal components)
• The first principal component (PC1) ? the
  greatest variance
• Example Fig 7.28 ? Fig 7.29 (principal
  component)
• (A) alluvial material in a dry stream valley
• (B) flat-lying quanternary and tertiary basalts
• (C) granite and granodiorite intrusion

14
Multi-image manipulation (cont.)

• Principal and canonical components (cont.)
• Intrinsic dimensionality (ID)
• Landsat MSS PC1PC2 explain 99.4 variance ? ID
  2
• PC4 depicts little more than system noise
• PC2 and PC3 illustrate certain features that were
  obscured by the more dominant patterns shown in
  PC1
• Semicircular feature in the upper right portion
• Principal ? Canonical
• Little prior information concerning a scene is
  available ? Principal
• Information about particular features of interest
  is known ? Canonical
• Fig 7.27b
• Three different analyst-defined feature types (D,
  ?, )
• Axes I and II ? maximize the separability of
  these classes and minimize the variance within
  each class
• Fig 7.30 Canonical component analysis

15
Multi-image manipulation (cont.)

• Vegetation components
• AVHRR
• VI (vegetation index)
• NDVI (normalized difference vegetation index)
• Landsat MSS
• Tasseled cap transformation (Fig 7.31)
• Brightness ? soil reflectance
• Greenness ? amount of green vegetation
• Wetness ? canopy and soil moisture
• TVI (transformed vegetation index)
• Fig 7.32, Fig 5.8, Plate 14
• TVI ? green biomass
• Precision crop management, precision farming,
  irrigation water, fertilizers, herbicides, ranch
  management, estimation of forage,
• GNDVI (green normalized difference vegetation
  index)
• Same formulation as NDVI, except the green band
  is substituted for the red band
• Leaf chlorophyll levels, leaf area index values,
  the photosynthetically active radiation absorbed
  by a crop canopy
• MODIS
• EVI (enhanced vegetation index)

16
Multi-image manipulation (cont.)

• Intensity-Hue-Saturation color space transform
• Fig 7.33 RGB color cube
• 28 ? 28 ? 28 16,777,216
• Gray line
• True color composite (B, G, R) ? false color
  composite (G, R, NIR)
• Fig 7.34 Planar projection of the RGB color cube
• Fig 7.35 Hexcone color model (RGB ? IHS)
• Intensity
• Hue
• Saturation
• Fig 7.36 advantage of HIS transform
• Data fusion Plate 19 (merger of IKONOS data)
• 1m panchromatic ? I?
• 4m multispectral ? RGB ? HIS
• Histogram matching I and I?
• I?HS ? R?G?B?

17
Tutorial mosaicking images

• Mosaicking (??)
• The art of combining multiple images into a
  single composite image
• No-georeferenced images
• Georeferenced images
• Feathering
• Edge feathering
• The edge is blended using a linear ramp that
  averages the two images across the specified
  distance
• Specified distance XX pixels, top image XX,
  bottom image XX
• Cutline feathering
• The annotation file must contain a polyline
  defining the cutline that is drawn from
  edge-to-edge and a symbol placed in the region of
  the image that will be cut off.

18
Tutorial mosaicking images (cont.)

• Pixel-Based Mosaicking
• Map ? Mosaicking ? Pixel Based
• Pixel Based Mosaic dialog
• Import ? Import Files
• avmosaic directory
• File dv06_2.img.
• Mosaic Input Files dialog
• File dv06_3.img.
• Mosaic Input Files dialog, hold down the Shift
  key and click on the dv06_2.img and dv06_3.img
  filenames to select them.
• Select Mosaic Size dialog
• X Size 614
• Y Size 1024
• Pixel Based Mosaic dialog, click on the
  dv06_3.img filename.
• YO 513
• File ? Apply
• Create a virtual mosaic
• File ? Save Template
• Output Mosaic Template
• Display the mosaicked image

19
Tutorial mosaicking images (cont.)

• Pixel-Based Mosaicking (cont.)
• Positioning two images into a composite mosaic
  image
• Options?Change Mosaic Size
• Select Mosaic Size dialog
• X Size 768
• Y Size 768
• Left-click within the green graphic outline of
  image 2
• Drag the 2 image to the lower right hand corner
  of the diagram.
• Right-click within the red graphics outline of
  image 3 and select Edit Entry
• Data Value to Ignore 0
• Feathering Distance 25
• Repeat the previous two steps for the other
  image.
• File ??Save Template
• Load Band
• No feathering is performed when using virtual
  mosaic.
• File ??Apply
• Background Value of 255
• Display
• Compare the virtual mosaic and the feathered
  mosaic using image linking and dynamic overlays

20
Tutorial mosaicking images (cont.)

• Map Based Mosaicking
• Map ? Mosaicking ? Georeferenced
• File ? Restore Template
• File lch_a.mos
• Optionally Input and Position Images
• Images will automatically be placed in their
  correct geographic locations The location and
  size of the georeferenced images will determine
  the size of the output mosaic.
• View the Top Image, Cutline and Virtual,
  Non-Feathered Mosaic
• Load Band lch_01w.img
• Right-click to display the shortcut menu and
  select Toggle ? Display Scroll Bars to turn on
  scroll bars
• Overlay ? Annotation
• File ? Restore Annotation
• File lch_01w.ann
• Load Band lch_02w.img
• File ? Open Image File
• File lch_a.mos
• Create the Output Feathered Mosaic
• File ? Apply
• Compare

21
Tutorial mosaicking images (cont.)

• Color Balancing During Mosaicking
• Create the Mosaic Image without Color Balancing
• Map ??Mosaicking ??Georeferenced
• Import ??Import Files
• Open File avmosaic directory, File
  mosaic1_equal.dat
• Open File avmosaic directory, File mosaic_2.dat
• select the mosaic_2.dat file, then hold down the
  Shift key and select the mosaic1_equal.dat file
• Show RGB color composites of these multispectral
  images
• Edit Entry
• Mosaic Display, choose RGB.
• For Red choose 1, for Green choose 2, and for
  Blue choose 3
• Repeat
• Two images are stretched independently

22
Tutorial mosaicking images (cont.)

• Color Balancing During Mosaicking (cont.)
• Output the Mosaic Without Color Balancing
• File ? Apply
• The seams between the two images are quite
  obvious
• Output the Mosaic With Color Balancing
• mosaic1_equal.dat
• Edit Entry.
• Color Balancing Adjust.
• mosaic_2.dat
• Edit Entry.
• Color Balancing Fixed
• File ? Apply.
• Color Balance using
• stats from overlapping regions/
• stats from complete files
• Display
• The seams between the two images are much less
  visible

23
Tutorial Data fusion

• Data Fusion
• The process of combining multiple image layers
  into a single composite image
• Enhance the spatial resolution of multispectral
  datasets using higher spatial resolution
  panchromatic data or singleband SAR data.
• Landsat TM and SPOT data fusion
• File ??Open External File ??IP Software ??ER
  Mapper
• Subdirectory lontmsp
• File lon_tm.ers
• Load RGB to display a true-color Landsat TM image
• File ??Open External File ??IP Software ??ER
  Mapper
• Subdirectory lontmsp
• File lon_spot.ers
• Load Band to display the gray scale SPOT image

24
Tutorial Data fusion (cont.)

• Landsat TM and SPOT data fusion (cont.)
• Resize Images to Same Pixel Size
• Check spatial dimensions (2820 x 1569) and (1007
  x 560)
• The Landsat data 28 meters
• The SPOT data 10 meters
• The Landsat image has to be resized by a factor
  of 2.8 to create 10 m data that matches the SPOT
  data
• Basic Tools ??Resize Data (Spatial/Spectral)
• choose the lon_tm image
• Resize Data Parameters
• Enter a value of 2.8 into the xfac text box
• Enter a value of 2.8009 into the yfac text box
• Tools ??Link ??Link Displays
• Perform Manual HSI Data Fusion
• Forward HSV Transform
• Transform ??Color Transforms ??RGB to HSV
• Select the resized TM data as the RGB image from
  the Display
• Display the Hue, Saturation, and Value images as
  gray scale images or an RGB.
• Create a Stretched SPOT Image to Replace TM Band
  Value
• Basic Tools ??Stretch Data

25
Tutorial Data fusion (cont.)

• Landsat TM and SPOT data fusion (cont.)
• Inverse HSV Transform
• Transform ??Color Transforms ??HSV to RGB
• Select the transformed TM Hue and Saturation
  bands as the H and S bands
• Choose the stretched SPOT data as the V band
• Display Results
• ENVI Automated HSV Fusion
• Transform ??Image Sharpening ??HSV from the ENVI
  main menu.
• Select Input RGB Input Bands dialog
• Choose the TM image RGB bands
• High Resolution Input File dialog
• Choose the SPOT image
• HSV Sharpening Parameters dialog
• File lontmsp.img
• Display Results, Link and Compare
• Color Normalized (Brovey) Transform
• Try the same process using
• Transform ??Image Sharpening ??Color Normalized
  (Brovey)

26
Tutorial Data fusion (cont.)

• SPOT PAN and XS fusion
• File ? Open Image File
• Subdirectory brestsp
• File s_0417_2.bil
• Load RGB to display a falsecolor infrared SPOT-XS
  image with 20 m spatial resolution
• File ? Open Image File
• File s_0417_1.bil
• Load Band to display the SPOT Panchromatic data.
• Resize Images to Same Pixel Size
• Check spatial dimensions (2835 x 2227) and (1418
  x 1114)
• The SPOT-XS image has to be resized by a factor
  of 2.0
• Basic Tools ? Resize Data (Spatial/Spectral)
• Choose the SPOTXS image (s_0417_2.bil)
• Resize Data Parameters dialog
• Enter a value of 1.999 into the xfac
• Enter a value of 1.999 into the yfac
• Tools ? Link ? Link Displays
• Fuse Using ENVI Methods
• Transform ? Image Sharpening ? HSV

27
Tutorial Data fusion (cont.)

• Landsat TM and SAR Data Fusion
• Read and Display Images
• File ? Open Image File
• Subdirectory rometm_ers
• File rome_ers2
• Load Band
• File ? Open Image File
• File rome_tm
• Load RGB to display a false-color infrared
  Landsat TM image with 30m spatial resolution
• Register the TM images to the ERS image
• Map ? Registration ? Select GCPs Image-to-Image
• Base Image Display 1 (the ERS data)
• Warp Image Display 2 (the TM data)
• File ? Restore GCPs from ASCII
• Ground Control Points Selection dialog
• GCP file rome_tm.pts
• Options ? Warp File
• File rome_tm
• Registration Parameters dialog

28
Tutorial Data fusion (cont.)

• Landsat TM and SAR Data Fusion (cont.)
• Perform HSI Transform to Fuse Data
• Transform ? Image Sharpening ? HSV
• Select Input RGB Input Bands
• High Resolution Input File dialog
• Choose the ERS-2 image
• Display and Compare Results