segmenting multi bands images by color and texture eldman o. nunes - aura conci ic - uff
TRANSCRIPT
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Segmenting multi bands images by color and
texture
Eldman O. Nunes - Aura Conci
IC - UFF
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Introduction
•Use of fractals and image multiespectral bands to characterize texture.
•Considering inter-relation among bands the image FD є [ 0 , number of bands + 2] .
•Improve the possibilies of usual false color segmentations (assigning satellite bands to RGB color). It is not now limited to 3 band.
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• The color sensations noticed by humans are combination of the intensities received by 3 types of cells cones.
• Combination of the 3 primary colors produces the others
• In the video: R=700 nm, G = 546,1 nm, B=435,1 nm.
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Monocromatic : one color channel or one band.
• binary image:
each pixel only
0 or 1 values.
• intensity level (grey level):
each pixel one value
from 0 to 255.
Digital images
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• Multiband images: n band value for each pixel.
• examples: »color images »sattelite images»medical images
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color images
each pixel 3 values ( from 0 to 255 )
3 bands: Red - Green -Blue.
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Band 1 Band 2 Band 3
Band 4 Band 5 Band 6 Band 7
example : a LandSat-7 image is a collection of 7 images of same scene
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sensor characteristics
TM HRV AVHRR
spacial resolution
30 m120 m (Band 6)
20 m (Band 1 a 3)10 m (Pan)
1.1 Km (nominal)
spectralBands (micro meters)
Band 1 - 0.45-0.52Band 2 - 0.52-0.60Band 3 - 0.63-0.69Band 4 - 0.76-0.90Band 5 - 1.55-1.75Band 6 - 10.74-12.5Band 7 - 2.08-2.35
Band 1 - 0.50-0.59Band 2 - 0.61-0.68Band 3 - 0.79-0.89Pan - 0.51-0.73
Band 1 - 0.58-0.68Band 2 - 0.725-1.1Band 3 - 3.55-3.93Band 4 - 10.30-11.30Band 5 - 11.50-12.50
Radiometric resolution
8 bits8 bits (1-3) 6 bits (Pan)
10 bits
Temporalresolution 16 days 26 days 2 times a days
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Landsat 7 - Sensor TM
Channel spectral band (um) main applications
1 0.45 - 0.52Differentiation between soil and vegetation, conifers and deciduous trees
2 0.52 - 0.60 healthy vegetation
3 0.63 - 0.69 chlorophyll absortion, vegetation types
4 0.76 - 0.90 biomass , water bodies
5 1.55 - 1.75 penetrate smokes, snow
6 10.4 - 12.5 surface temperature from -100 to 150 C
7 2.08 - 2.35 hidrotermal map, buildings, soil trafficability
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Band 4 (R), 5 (G), 3 (B)
Band 4 (R), 3 (G), 2 (B)
Multiespectral false color :
l , m, n Bands to Red, Green and Blue.
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TexturesTexture is characterized by the repetition of a model on an area.
Textons : size, format, color and orientation of the elements.
Textons can be repeated in an exact way or with small variations on a same theme.
Texture 1
Texture 2
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Fractal Geometry
• self similar sets
• fractal dimensions and measures used to classify textures
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FD for binary image
• Box Counting Theorem - 2D images.
• For a set A, Nn(A) = number of boxes of side 1/2n
which interser the set A:
DF = lim n log Nn (A) / log 2n
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n Nn (A) 2n log Nn (A) log 2n
1 4 2 1,386 0,693
2 12 4 2,484 1,386
3 36 8 3,583 2,079
4 108 16 4,682 2,772
5 324 32 5,780 3,465
6 972 64 6,879 4,158
0
2
4
6
8
0 1 2 3 4 5
log (2n )
log
Nn
(A)
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gray level images• Box Counting Theorem extension for 3-dimensional object: third
coordinate represents the intensity of the pixel.
• DF between 2 e 3.
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Blanket Dimension - Blanket Covering Method
The space is subdivided in cubes of sides SxSxS ’.
Nn(A) denotes the number of cubes intercept a blanket covering the image: Nn = nn (i,j)
On each grid (i,j), nn (i,j) = int ( ( max – min ) / s’ ) + 1
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for multi-bands image
•a color R G B image is a subset of the 5-dimensional space : N5). Each pixel is defined by: (x, y, r, g, b)
•FD of this images: values from 2 to 5.
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Generalizing: d-cube
• points (0D), segments (1D), squares (2D), cubes (3D) and
• for a n-dimensional : n-cube (nD)
• But what is d-cubos , and how many d-cubes appear in a divison of Nd space?
r
r
r
rr
r
SEGMENTO
QUADRADO CUBO
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Sweep representation :
• n-cube as translational swepps of (n-1) cube
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Generalizing: d-Cube Counting - DCC:
• the experimental determination of the fractal dimension of images with multiple channels;
• will imply in the recursive division of the N space in d-cubes of size r;
• followed by the contagem of the numbers of d-cubes that intercept the image.
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• monochrome images: the space N3 is divided by 3-cubos of size 1/2n, and the number of 3-cubos that intercept the image it is counted.
• color images: the space N5 is divided by 5-cubos of the same size 1/2n, and the number of 5-cubos that intercept the image is counted.
• satellite images: the space Nd is divided by d-cubes of size 1/2n and the number of d-cubes that intercept the image is counted.
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• number of 1-cubes: Nn
1-cubos = 2 1x n, where n is the number of divisions.
• number of 2-cubes: Nn
2-cubos = 2 2x n, where n is the number of divisions.
• number of 3-cubos: Nn
3-cubos = 2 3x n, where n is the number of divisions.
• Generalizing, the number of identical d-cube: Nn
d-cubes = 2 d x n, where d is the space dimension and n it is the number of divisions.
Then FD of d-dimensional images can be obtained by:
DFn = log (Nn,d-cubo) /log (2n )
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Results binary images
gray scale
colored images
satellite images
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CDC invariance to resolution (FD 3,465)
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CDC invariance on colors reflection (second image) and affine transformations (FD 3,465)
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CDC invariance to band combinations(FD 3,465) : RGB (4-5-6, 4-6-5, 5-4-6, 5-6-4, 6-4-5, 6-5-4)
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Mosaic of textures: original x CDCSegmentation result: same color means same texture.
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comparison: original - SEGWINSPRING - CDC
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Region on the city of Patriocínio - MG
(from Landsat 5-TM, 5-4-3 spectral band to RGB)
Segmentation results by CDC