1. Segmenting multi bands images by color and texture Eldman O. Nunes - Aura Conci IC - UFF

2. 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). I t is not now limited to 3 band.

3. 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.

4. 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

5. Multiband images: n band value for each pixel. examples: »color images »sattelite images »medical images

6. color images each pixel 3 values ( from 0 to 255 ) 3 bands: Red - Green -Blue.

7. Band 1Band 2Band 3 Band 4Band 5Band 6Band 7 example : a LandSat-7 image is a collection of 7 images of same scene

8. sensor characteristics TMHRVAVHRR spacial resolution 30 m 120 m (Band 6) 20 m (Band 1 a 3) 10 m (Pan) 1.1 Km (nominal) spectral Bands (micro meters) Band 1 - 0.45-0.52 Band 2 - 0.52-0.60 Band 3 - 0.63-0.69 Band 4 - 0.76-0.90 Band 5 - 1.55-1.75 Band 6 - 10.74-12.5 Band 7 - 2.08-2.35 Band 1 - 0.50-0.59 Band 2 - 0.61-0.68 Band 3 - 0.79-0.89 Pan - 0.51-0.73 Band 1 - 0.58-0.68 Band 2 - 0.725-1.1 Band 3 - 3.55-3.93 Band 4 - 10.30-11.30 Band 5 - 11.50-12.50 Radiometric resolution 8 bits 8 bits (1-3) 6 bits (Pan) 10 bits Temporal resolution 16 days26 days2 times a days

9. Landsat 7 - Sensor TM Channelspectral band (um)main applications 10.45 - 0.52 Differentiation between soil and vegetation, conifers and deciduous trees 20.52 - 0.60 healthy vegetation 30.63 - 0.69 chlorophyll absortion, vegetation types 40.76 - 0.90 biomass, water bodies 51.55 - 1.75 penetrate smokes, snow 610.4 - 12.5 surface temperature from -100 to 150 C 72.08 - 2.35 hidrotermal map, buildings, soil trafficability

10. 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.

11. Textures Texture 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

12. Fractal Geometry self similar sets fractal dimensions and measures used to classify textures

13. FD for binary image Box Counting Theorem - 2D images. For a set A, N n (A) = number of boxes of side 1/2 n which interser the set A: DF = lim n  log N n (A) / log 2 n

14. n N n (A) 2n2n log N n (A) log 2 n 1 421,3860,693 2 1242,4841,386 3 3683,5832,079 4 108164,6822,772 5 324325,7803,465 6 972646,8794,158

15. gray level images Box Counting Theorem extension for 3-dimensional object: third coordinate represents the intensity of the pixel. DF between 2 e 3.

16. 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: N n =  n n (i,j) On each grid (i,j), n n (i,j) = int ( ( max – min ) / s’ ) + 1

17. for multi-bands image a color R G B image is a subset of the 5-dimensional space : N 5 ). Each pixel is defined by: (x, y, r, g, b) FD of this images: values from 2 to 5.

18. 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 N d space?

19. Sweep representation : n-cube as translational swepps of (n-1) cube

20. 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.

21. monochrome images: the space N 3 is divided by 3-cubos of size 1/2 n, and the number of 3- cubos that intercept the image it is counted. color images: the space N 5 is divided by 5- cubos of the same size 1/2 n, and the number of 5-cubos that intercept the image is counted. satellite images: the space N d is divided by d- cubes of size 1/2 n and the number of d-cubes that intercept the image is counted.

22. number of 1-cubes: N n 1-cubos = 2 1x n, where n is the number of divisions. number of 2-cubes: N n 2-cubos = 2 2x n, where n is the number of divisions. number of 3-cubos: N n 3-cubos = 2 3x n, where n is the number of divisions. Generalizing, the number of identical d-cube: N n 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: DF n = log (N n,d-cubo ) /log (2 n )

23. Results  binary images  gray scale  colored images  satellite images

24. CDC invariance to resolution (FD  3,465)

25. CDC invariance on colors reflection (second image) and affine transformations (FD  3,465)

26. 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)

27. Mosaic of textures: original x CDC Segmentation result: same color means same texture.

28. comparison: original - SEGWIN SPRING - CDC

29. Region on the city of Patriocínio - MG (from Landsat 5-TM, 5-4-3 spectral band to RGB) Segmentation results by CDC