Transcript Image Enhancement

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: 7 July 2015
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
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
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
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
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
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
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
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
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
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
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
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
Multi-image manipulation (cont.)
 Principal and canonical components (cont.)
• Intrinsic dimensionality (ID)
Landsat MSS: PC1+PC2 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
Multi-image manipulation (cont.)
 Vegetation components
• AVHRR
 VI (vegetation index)
VI  Ch2  Ch1
 NDVI (normalized difference vegetation index)
• Landsat MSS
NDVI 
Ch2  Ch1
Ch2  Ch1
 Tasseled cap transformation (Fig 7.31)
 Brightness  soil reflectance
 Greenness  amount of green vegetation
 Wetness  canopy and soil moisture
 TVI (transformed vegetation index)
 DN NIR  DN red

TVI  
 0.5
 DN NIR  DN red

1/ 2
 Fig 7.32, Fig 5.8, Plate 14
100
 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)
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΄
Tutorial: mosaicking images
 Mosaicking （鑲嵌）
• 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 edgeto-edge and a symbol placed in the region of the image that will be cut off.
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
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
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
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
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
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
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
 File: lon_spot
 Data Stretching
 Output Data: 0 for the Min and 1.0 for the Max
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)
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



Select Input RGB Input Bands
High Resolution Input File
HSV Sharpening Parameters dialog
 Display and Compare Results
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
 Change Output Parameters




Enter 1 for the Upper Left Corner (XO),
Enter 1 for the Upper Left Corner (YO)
Enter 5134 for the Number of Samples
Enter 5549 for the Number of Lines
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