Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide...
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Transcript of Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide...
![Page 1: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/1.jpg)
Spatial-based Enhancements
Lecture 3
prepared by R. Lathrop 10/99
updated 10/03
ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184
![Page 2: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/2.jpg)
Spatial frequency
• Spatial frequency is the number of changes in brightness value per unit distance in any part of an image
• low frequency - tonally smooth, gradual changes
• high frequency - tonally rough, abrupt changes
![Page 3: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/3.jpg)
Spatial Frequencies
Zero Spatial frequency Low Spatial frequency High Spatial frequency
Example from ERDAS IMAGINE Field Guide, 5th ed.
![Page 4: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/4.jpg)
Spatial vs. Spectral Enhancement
• Spatial-based Enhancement modifies a pixel’s values based on the values of the surrounding pixels (local operator)
• Spectral-based Enhancement modifies a pixel’s values based solely on the pixel’s values (point operator)
![Page 5: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/5.jpg)
Moving Window concept
Kernel scans across row, then down a row and across again, and so on.
![Page 6: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/6.jpg)
Focal Analysis
• Mathematical calculation of pixel DN values within moving window
• Mean, Median, Std Dev., Majority
• Focal value written to center pixel in moving window
![Page 7: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/7.jpg)
Example: noise filtering
![Page 8: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/8.jpg)
Texture
• Texture: variation in BV’s in a local region, gives estimate of local variability. Can be used as another layer of data in classification/ interpretation process.
• 1st order statistics: range, variance, std dev
• Window size will affect results
![Page 9: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/9.jpg)
Texture: variance
3x3 texture 7x7 texture
![Page 10: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/10.jpg)
Pixel ConvolutionBV = int [ SUM i->q (SUM j->q fij dij) ]
---------------------------------- F
where
i = row location j = column location
fij = the coefficient of a convolution kernel at position i, j
dij = the BV of the original data at position i, j
q = the dimension of the kernel, assuming a square kernel
F = either the sum of the coefficients of the kernel or 1 if the sum of coefficients is zero
BV = output pixel value
![Page 11: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/11.jpg)
Example: kernel convolution
8 8 6 6 6
2 8 6 6 6
2 2 8 6 6
2 2 2 8 6
2 2 2 2 8
-1 -1 -1
-1 16 -1
-1 -1 -1
Example from ERDAS IMAGINE Field Guide, 5th ed.
Convolution Kernel
![Page 12: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/12.jpg)
Example: kernel convolution
Kernel: -1 -1 -1 -1 16 -1 -1 -1 -1
Original: 8 6 6 2 8 6 2 2 8
XResult
= 11
J=1 j=2 j=3
I=1 (-1)(8) + (-1)(6) + (-1)(6) = -8 -6 -6 = -20
I=2 (-1)(2) + (16)(8) + (-1)(6) = -2 +128 -6 = 120
I=3 (-1)(2) + (-1)(2) + (-1)(8) = -2 -2 -8 = -12
F = 16 - 8 = 8 Sum = 88
output BV = 88 / 8 = 11
![Page 13: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/13.jpg)
11 6 6 6 6
0 11 6 6 6
2 0 11 6 6
2 2 0 11 6
2 2 2 0 11
Example: kernel convolution
8 6 6 6 6
2 8 6 6 6
2 2 8 6 6
2 2 2 8 6
2 2 2 2 8
Input Output
Edge
![Page 14: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/14.jpg)
Low vs. high spatial frequency enhancements
• Low frequency enhancers (low pass filters):Emphasize general trends, smooth image
• High frequency enhancers (high pass filters):Emphasize local detail, highlight edges
![Page 15: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/15.jpg)
Example: Low Frequency EnhancementKernel: 1 1 1
1 1 11 1 1
Original: 204 200 197 201 100 209 198 200 210
Output: 204 200 197 201 191 209 198 200 210
Original: 64 50 57 61 125 69 58 60 70
Output: 64 50 57 61 65 69 58 60 70
Low value surrounded by higher values
High value surrounded by lower values
From ERDAS Field Guide p.111
![Page 16: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/16.jpg)
Low pass filter
Orignal IKONOS pan 7x7 low pass
![Page 17: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/17.jpg)
Example: High Frequency EnhancementKernel: -1 -1 -1
-1 16 -1-1 -1 -1
Original: 204 200 197 201 120 209 198 200 199
Output: 204 200 197 201 39 209 198 200 210
Original: 64 50 57 61 125 69 58 60 70
Output: 64 50 57 61 187 69 58 60 70
Low value surrounded by higher values
High value surrounded by lower values
From ERDAS Field Guide p.111
![Page 18: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/18.jpg)
High Pass filter
3x3 high pass 3x3 edge enhance -1 -1 -1 -1 17 -1 -1 -1 -1
-1 -1 -1 -1 9 -1 -1 -1 -1
![Page 19: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/19.jpg)
Edge detection
• Edge detection process: Smooth out areas of low spatial frequency and highlight edges (local changes) only
• 1) calculating spatial derivatives (differencing)
• 2) edge detecting template (Zero-sum kernels):- directional (compass templates)- non-directional (Laplacian)
• 3) subtracting a smoothed image from the original
![Page 20: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/20.jpg)
Linear Edge Detection techniques
• Directional gradient filters produce output images whose BVs are proportional to the difference between neighboring pixel BVs in a given directional, i.e. they calculate the directional gradient
• Spatial differencing:Vertical: BVi,j = BVi,j - BVi,j+1 + KHorizontal: BVi,j = BVi,j - Bvi-1,j + Kconstant K added to make output positive
![Page 21: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/21.jpg)
Zero sum kernels
• Zero sum kernels: the sum of all coefficients in the kernel equals zero. In this case, F is set = 1 since division by zero is impossible
• zero in areas where all input values are equal
• low in areas of low spatial frequency
• extreme in areas of high spatial frequency (high values become higher, low values lower)
![Page 22: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/22.jpg)
Example: Linear Edge Detecting Templates
Vertical: -1 0 1 Horizontal: -1 -1 -1-1 0 1 0 0 0 -1 0 1 1 1 1
Diagonal Diagonal(NW-SE): 0 1 1 (NE-SW): 1 1 0
-1 0 1 1 0 -1 -1 -1 0 0 -1 -1
Example: vertical template convolution
Original: 2 2 2 8 8 8 Output: 0 18 18 0 0 2 2 2 8 8 8 0 18 18 0 0 2 2 2 8 8 8 0 18 18 0 0
![Page 23: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/23.jpg)
Linear Edge Detection
Horizontal Edge Vertical Edge
-1 -2 -1 0 0 0 1 2 1
-1 0 1 -2 0 2 -1 0 1
![Page 24: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/24.jpg)
Linear Line Detecting Templates
• Line features (i.e. rivers and roads) can be detected as pairs of edges if they are more than one pixel wide (using linear edge detection templates). If they are a single pixel wide, they can be detected using these templates:
• Vertical: -1 2 -1 Horizontal: -1 -1 -1 -1 2 -1 2 2 2 -1 2 -1 -1 -1 -1
![Page 25: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/25.jpg)
Example: Linear Line Detecting Templates
Vertical: -1 2 -1 Horizontal: -1 -1 -1-1 2 -1 2 2 2 -1 2 -1 -1 -1 -1Original
2 2 2 8 2 2 22 2 2 8 2 2 22 2 2 8 2 2 2
Linear Edge Detection. 0 18 0 18 0 .. 0 18 0 18 0 .. 0 18 0 18 0 .
Linear Line Detection. 0 -6 12 -6 0 .. 0 -6 12 -6 0 .. 0 -6 12 -6 0 .
![Page 26: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/26.jpg)
Linear Line Detection
Horizontal Edge Vertical Edge
-1 -1 -1 2 2 2 -1 -1 -1
-1 2 -1 -1 2 -1 -1 2 -1
![Page 27: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/27.jpg)
Compass gradient masks
Produce a maximum output for vertical (or horizontal) brightness value changes from the specified direction. For example a North compass gradient mask enhances changes that increase in a northerly direction, i.e. from south to north:
North: 1 1 11 -2 1
-1 -1 -1
![Page 28: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/28.jpg)
Example: Compass gradient masks
North: 1 1 1 South: -1 -1 -11 -2 1 1 -2 1
-1 -1 -1 1 1 1
Example: North vs. south gradient mask
North SouthOriginal: 8 8 8 Output: . . . Output: . . .
8 8 8 0 0 0 0 0 0 8 8 8 18 18 18 -18 -18 -18 2 2 2 18 18 18 -18 -18 -18 2 2 2 0 0 0 0 0 0 2 2 2 . . . . . .
![Page 29: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/29.jpg)
Non-directional Edge Enhancement
• Laplacian is a second derivative and is insensitive to direction. Laplacian highlights points, lines and edges in the image and suppresses uniform, smoothly varying regions
• 0 -1 0 1 -2 1-1 4 -1 -2 4 -2
0 -1 0 1 -2 1
![Page 30: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/30.jpg)
Nonlinear Edge Detection
Sobel edge detector: a nonlinear combination of pixels
Sobel = SQRT(X2 + Y2)
X: -1 0 1 Y: 1 2 1 -2 0 2 0 0 0 -1 0 1 -1 -2 -1
![Page 31: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/31.jpg)
Nondirectional edge filter
Laplacian filter Sobel filter
![Page 32: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/32.jpg)
Edge enhancement
• Edge enhancement process:
• First detect the edges
• Add or subtract the edges back into the original image to increase contrast in the vicinity of the edge
![Page 33: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/33.jpg)
Original IKONOS pan
Edge enhancement
Laplacian
-
Original – edge = edge enhanced
![Page 34: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/34.jpg)
Original IKONOS pan Unsharp masking to enhance detail
7x7 low
-
Original – low pass = edge enhanced
![Page 35: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/35.jpg)
Edge Mapping
• BV thresholding of the edge detector output to create a binary map of edges vs. non-edges
• Threshold too low: too many isolated pixels classified as edges and edge boundaries too thick
• Threshold too high: boundaries will consist of thin, broken segments
![Page 36: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/36.jpg)
Fourier Transform
• Fourier analysis is a mathematical technique for separating an image into its various spatial frequency components.
• Can display the frequency domain to view magnitude and directional of different frequency components, can then filter out unwanted components and back-transform to image space.
• Global rather than local operator• Useful for noise removal
![Page 37: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/37.jpg)
Fourier Analysis Example
Side scan sonar image of sea bottom
Fourier spectrum
![Page 38: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/38.jpg)
Fourier Analysis Example
Fourier spectrum
Low frequencies towards center
High frequencies towards edges
Image noise often shows as thin line, oriented perpendicular to original image
![Page 39: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/39.jpg)
Fourier Analysis Example
Low pass filter Back transformed image
![Page 40: Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.](https://reader035.fdocuments.in/reader035/viewer/2022062800/56649e0a5503460f94af250f/html5/thumbnails/40.jpg)
Fourier Analysis Example
Wedge filter Back transformed image