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Study on Segmentation of Color Remote Sensing Image
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Procedia Engineering
ProcediaProcedia Engineering 00 (2011) Engineering 29 000–000 (2012) 3312 – 3316 www.elsevier.com/locate/procedia
2012 International Workshop on Information and Electronics Engineering (IWIEE)
Study on Segmentation of Color Remote Sensing Image Wu-jian* Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R.China
Abstract A multi-region image segmentation method for color remote sensing images has been proved in the thesis, which will make preprocessing to color remote images firstly, and then split the target regions combining with K-mean clustering algorithm and region growing segmentation algorithm. At last mathematical morphology is taken into consideration in this method to improve the accuracy of segmentation results. Experimental results show that the proposed method can achieve a better color remote sensing image segmentation effect than others.
© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology Keywords: Color remote sensing images; K-mean clustering; Region growing algorithm; Mathematical morphology;
1. Introduction It has important research value and practical significance to separate out the target area from the color remote sensing images accurately as color remote sensing image segmentation technology has become a hotspot in the image processing field. A method taking advantages of both K-mean clustering algorithm and region growing algorithm has been used to split the target region of the color remote sensing image. The image must be converted to HSV color model at first when segmentation begins, meanwhile, the characteristic information such as color and texture is also taken into consideration.
* Corresponding author. Tel.: +1-399-615-2855; fax: +0-236-247-1435. E-mail address: [email protected] Supported in part by funding from the National Natural Science Foundation of China (610-71118) and the National Natural Science Foundation of Chongqing University of Posts and Communications (A2009-62);
1877-7058 © 2011 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.01.486
3313
Wu-jian / Procedia – 3316 Wu-jian/ ProcediaEngineering Engineering29 00(2012) (2011)3312 000–000
2
2. Image Preprocessing In order to improve the quality of color image segmentation, reduce the calculated amount in segmentation process, it is necessary to make pre-processing to target color images. 2.1. Vector Median Filter In practical engineering applications, the resulting color images generally contain noises due to the interference caused by certain factors. Generally, it’s necessary to do smoothing for the image containing noise, thus enhancing the image segmentation effect. The vector median filter algorithm is used to do the filter processing for the original image in the thesis. 2.2. Color Quantization Multiple quantization algorithms are proposed to dispose the image, and then choose the most efficient one through comparing the results of these algorithms. Two main evaluation indexes of color quantization results of color image are time complexity and image distortion. And that image distortion is the uppermost evaluation index of color quantization algorithm, and whose definition is as follows: 1 N = E (1) ∑ ⎡⎣(ΔLi* ) 2 + (Δai * ) 2 + ( Δbi * ) 2 ⎤⎦ N i =1 * * * Where N is the total number of pixels in the image; ΔLi 、 Δai 、 Δbi are separately the brightness difference and chromaticity difference of point i between original color image and the image after the quantification processing in the Lab color space. The list below is the performance comparison of variety of color quantization algorithms. Table 1. List of performance comparison of color quantization algorithms Algorithm
Fashion-color
Minimum variance
Color approximate
The original color num/color num after quantification
63.6：1
63.6：1
63.6：1
Distortion degree of image
4.5865
2.8132
15.5226
>5
0.6563
0.6250
Run time
From the analysis above, we come to the conclusion that the minimum variance quantification algorithm is the most efficient one in quantification. 3. Image Segmentation Region growing algorithm can directly acts on the color space, taking color distribution into consideration in the process of splitting. Therefore, this thesis applied the region growing segmentation algorithm into the target area taking the color and texture feature information of the color remote sensing image into account.
3.1. Using K-mean Clustering Algorithm to Select Seed Point In order to improve the automation degree of image segmentation, K-mean clustering algorithm to select original growth point of each target area in the remote sensing image automatically. As the
3314
/ Procedia Engineering (2012) 3312 – 3316 Author Wu-jian name / Procedia Engineering 0029 (2011) 000–000
3
clustering effect affects the selection, and in order to find reasonable clustering number, two parameters, the average color divergence and the maximum distance between classes are defined firstly. When the clustering area numbers reach K, the color divergence is defined as: N
K
∑∑ J =
r
(L − L *r
*r
i
) + (a − a 2
mean
*r
*r
i
mean
) + (b − b ) 2
*r
*r
i
mean
2
(2)
r 0= i 0 = N
∑
(L − L *
i
* mean
) + (a − a 2
*
*
i
mean
) + (b − b ) 2
*
*
i
mean
2
i=0
Where J is the average color divergence of the image, N is the total number of pixels, N r is the *r *r *r number of pixels in the clustering r, Li 、 ai 、 bi are separately brightness and chrominance *r *r *r components of point i in the clustering r, Lmean 、 amean and bmean are separately the mean value of * * * brightness and chrominance components in the clustering r, Lmean 、 amean and bmean are separately the mean value of brightness and chrominance components of the whole image. When the clustering area numbers reach K, the maximum distance between classes is:
= Lmax {max( Lmean + amean + bmean ) − min( Lmean + amean + bmean= ) | r 12 ，， L，K} *r
*r
*r
2
*r 2
*r 2
*r
2
*r 2
*r 2
(3)
*r
Where， Lmean 、 amean and bmean are separately the mean value of brightness and chrominance components in the clustering r. In the clustering process, when J converges at a certain minimum value, while Lmax converges at a maximum value, the clustering effect achieves the best result, that is parameter V converges at a certain minimum value, and the expression is as follows: V = J / Lmax (4) The figure below is the variation trend when clustering number K fetches different values, as follows: From the figure in the left, it is clear to know that when clustering number exceed 5, the variation trend of parameter V is gradually gentle, and when the clustering number is relatively small, it is favorable to the selection of seed point backwards. So the clustering number should be 5. Each data object in the K-mean clustering algorithm is in the form as follows: Fig. 1. Variation trend of parameter V as clustering number changes
{
S = L 、a 、b *
*
*
}
(5)
Next, the centroid of corresponding clustering area of each target region was selected as seed point of the target region in the thesis, and then calculated 3×3 neighborhood characteristic mean value of the seed point as the seed point’s initial value, thus properly avoiding the influence of seed point selection error and the noise. If the centroid is not an integer, round to integers, and the seed point of clustering region r can be defined as follows:
( ⎡⎣∑ Mr
( x ，y
) =
⎤ ，⎡∑ y ⎦ ⎣ Mr
x
N
r0 r0 i r =i 1=i 1
i
N
r
⎤ ⎦
)
r=1，L ，； 4
(6)
Where M r is the pixel number of clustering r, xi and yi are the coordinate value in the image of pixel i of the region (taking upper left corner as the origin of coordinate, and downwards as X axis, the right side as Y axis ) .
3315
Wu-jian / Procedia – 3316 Wu-jian/ ProcediaEngineering Engineering29 00(2012) (2011)3312 000–000
4
3.2. Regional Growth In the process of regional growth, it is necessary to compare the distance of pixel (i,j) to the mean value of color and texture in a certain area, according to equation (2.1) and (2.2), distances can be defined as follows: 2 2 2 ] + w2 [ S (i, j ) − S avg ] + w3 [V (i, j ) − Vavg ] ⎤⎦ ⎣ [ DGra (i= , j ; avg ) HH (i，j ) − HH avg
DCol (i , j ; = avg ) ⎡ w1 H (i , j ) − H avg
1/ 2
(7) (8)
Where H (i , j ) 、 S (i , j ) and V (i , j ) are separately H 、 S and V components of point(i,j) after normalization, H avg 、 S avg and V avg are the H 、 S 、 V mean value of pixels that have been added to the growing region, HH (i，j ) is the information entropy of point(i,j) in the image, HH avg is the mean value of information entropy of pixels that have added to the growing region, w1 、 w2 and w3 are three weight coefficients. According to the equation (9) and (10)，weighted distance can be defined as follows:： = DF (i , j ; avg ) w4 DCol (i , j ; avg ) + w5 DGra (i , j ; avg ) (9) Where, w4 、 w5 are weight coefficients. Based on the definition of distance above, the region growing rule is defined as follows: DF (i , j ; avg ) < T1
(10)
And T1 is the threshold condition of the region segmentation. Then the seed point calculated above can be used to do the regional growth. 4. Process after Segmentation Results
Making morphology processing and holes filling to the segmentation result got through the regional growth algorithm can make the edge of segmentation result more smoothing, and this process is significant to the image segmentation. To the segmentation result of the target area 2 and 3, one time hole filling was made firstly, and then a square structural element with length of side 3 is used to do an open operation to the segmentation result, and then a closing operation was made, at last, the second time hole filling operation to the segmentation result was made to fill the small meaningless holes. It is worth notice that if the target area does not have a single simply connected region, it is unreasonable to make hole filling to the segmentation result of target area. Figure 2 is the segmentation result after the morphology processing and hole filling above.
Fig. 2. (a) Morphology processing result (binary image); (b) Morphology processing result (color image)
From the figure above, the segmentation result after processing reaches the expected effect.
3316
/ Procedia Engineering (2012) 3312 – 3316 Author Wu-jian name / Procedia Engineering 0029 (2011) 000–000
5. Experimental Results and Analysis
On the basis of the segmentation algorithm above, segmentation experiment was made on two interested areas of color remote sensing image. The size of the first piece of the original remote sensing image is 450 × 266, and 4 seed point positions were identified through K-mean clustering algorithm respectively. According to the regional growing rules, the segmentation result was finally achieved through configuring different threshold condition firstly, and then doing mathematical morphology processing and edge segmentation to the result from the last step. Segmentations result is shown in figure 3 and figure 4, as follows:
Fig. 3. (a) Target region segmentation result; (b) Corresponding contour line of segmentation result
Fig. 4. (a) Target region segmentation result; (b) Corresponding contour line of segmentation result
6. Conclusion
Color remote sensing image is the main study object in the thesis, and a color image segmentation combining K-mean clustering algorithm with regional growth algorithm is put forward, which can be used to segment multi-areas, and the effect of segmentation is satisfying. But there are still some defects to be improved. Reference [1] LI Gang, JIN Rong, ZHU Lei, WANG Lin-lin. Color remote sensing image edge detection algorithm based on multilateral fuzzy mathematical morphology . Microelectronics and Computer. 2009, 26(8). [2] GANG SA Lei-si. Digital image processing (2th edition). BeiJing: Electronic Industry Press, 2003. 306-420. [3] HAN Jin-yu, WANG Shou-zhi. Color image denoising algorithm based on noise character and vector median filter . Computer Application. 2009, 29(9). [4] ZHANG Hong-lin. Proficiency in the typical algorithm and realization of Visual C++ digital image processing. BeiJing: Posts & Telecom Press, 2008.
5
Procedia Engineering
ProcediaProcedia Engineering 00 (2011) Engineering 29 000–000 (2012) 3312 – 3316 www.elsevier.com/locate/procedia
2012 International Workshop on Information and Electronics Engineering (IWIEE)
Study on Segmentation of Color Remote Sensing Image Wu-jian* Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R.China
Abstract A multi-region image segmentation method for color remote sensing images has been proved in the thesis, which will make preprocessing to color remote images firstly, and then split the target regions combining with K-mean clustering algorithm and region growing segmentation algorithm. At last mathematical morphology is taken into consideration in this method to improve the accuracy of segmentation results. Experimental results show that the proposed method can achieve a better color remote sensing image segmentation effect than others.
© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology Keywords: Color remote sensing images; K-mean clustering; Region growing algorithm; Mathematical morphology;
1. Introduction It has important research value and practical significance to separate out the target area from the color remote sensing images accurately as color remote sensing image segmentation technology has become a hotspot in the image processing field. A method taking advantages of both K-mean clustering algorithm and region growing algorithm has been used to split the target region of the color remote sensing image. The image must be converted to HSV color model at first when segmentation begins, meanwhile, the characteristic information such as color and texture is also taken into consideration.
* Corresponding author. Tel.: +1-399-615-2855; fax: +0-236-247-1435. E-mail address: [email protected] Supported in part by funding from the National Natural Science Foundation of China (610-71118) and the National Natural Science Foundation of Chongqing University of Posts and Communications (A2009-62);
1877-7058 © 2011 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.01.486
3313
Wu-jian / Procedia – 3316 Wu-jian/ ProcediaEngineering Engineering29 00(2012) (2011)3312 000–000
2
2. Image Preprocessing In order to improve the quality of color image segmentation, reduce the calculated amount in segmentation process, it is necessary to make pre-processing to target color images. 2.1. Vector Median Filter In practical engineering applications, the resulting color images generally contain noises due to the interference caused by certain factors. Generally, it’s necessary to do smoothing for the image containing noise, thus enhancing the image segmentation effect. The vector median filter algorithm is used to do the filter processing for the original image in the thesis. 2.2. Color Quantization Multiple quantization algorithms are proposed to dispose the image, and then choose the most efficient one through comparing the results of these algorithms. Two main evaluation indexes of color quantization results of color image are time complexity and image distortion. And that image distortion is the uppermost evaluation index of color quantization algorithm, and whose definition is as follows: 1 N = E (1) ∑ ⎡⎣(ΔLi* ) 2 + (Δai * ) 2 + ( Δbi * ) 2 ⎤⎦ N i =1 * * * Where N is the total number of pixels in the image; ΔLi 、 Δai 、 Δbi are separately the brightness difference and chromaticity difference of point i between original color image and the image after the quantification processing in the Lab color space. The list below is the performance comparison of variety of color quantization algorithms. Table 1. List of performance comparison of color quantization algorithms Algorithm
Fashion-color
Minimum variance
Color approximate
The original color num/color num after quantification
63.6：1
63.6：1
63.6：1
Distortion degree of image
4.5865
2.8132
15.5226
>5
0.6563
0.6250
Run time
From the analysis above, we come to the conclusion that the minimum variance quantification algorithm is the most efficient one in quantification. 3. Image Segmentation Region growing algorithm can directly acts on the color space, taking color distribution into consideration in the process of splitting. Therefore, this thesis applied the region growing segmentation algorithm into the target area taking the color and texture feature information of the color remote sensing image into account.
3.1. Using K-mean Clustering Algorithm to Select Seed Point In order to improve the automation degree of image segmentation, K-mean clustering algorithm to select original growth point of each target area in the remote sensing image automatically. As the
3314
/ Procedia Engineering (2012) 3312 – 3316 Author Wu-jian name / Procedia Engineering 0029 (2011) 000–000
3
clustering effect affects the selection, and in order to find reasonable clustering number, two parameters, the average color divergence and the maximum distance between classes are defined firstly. When the clustering area numbers reach K, the color divergence is defined as: N
K
∑∑ J =
r
(L − L *r
*r
i
) + (a − a 2
mean
*r
*r
i
mean
) + (b − b ) 2
*r
*r
i
mean
2
(2)
r 0= i 0 = N
∑
(L − L *
i
* mean
) + (a − a 2
*
*
i
mean
) + (b − b ) 2
*
*
i
mean
2
i=0
Where J is the average color divergence of the image, N is the total number of pixels, N r is the *r *r *r number of pixels in the clustering r, Li 、 ai 、 bi are separately brightness and chrominance *r *r *r components of point i in the clustering r, Lmean 、 amean and bmean are separately the mean value of * * * brightness and chrominance components in the clustering r, Lmean 、 amean and bmean are separately the mean value of brightness and chrominance components of the whole image. When the clustering area numbers reach K, the maximum distance between classes is:
= Lmax {max( Lmean + amean + bmean ) − min( Lmean + amean + bmean= ) | r 12 ，， L，K} *r
*r
*r
2
*r 2
*r 2
*r
2
*r 2
*r 2
(3)
*r
Where， Lmean 、 amean and bmean are separately the mean value of brightness and chrominance components in the clustering r. In the clustering process, when J converges at a certain minimum value, while Lmax converges at a maximum value, the clustering effect achieves the best result, that is parameter V converges at a certain minimum value, and the expression is as follows: V = J / Lmax (4) The figure below is the variation trend when clustering number K fetches different values, as follows: From the figure in the left, it is clear to know that when clustering number exceed 5, the variation trend of parameter V is gradually gentle, and when the clustering number is relatively small, it is favorable to the selection of seed point backwards. So the clustering number should be 5. Each data object in the K-mean clustering algorithm is in the form as follows: Fig. 1. Variation trend of parameter V as clustering number changes
{
S = L 、a 、b *
*
*
}
(5)
Next, the centroid of corresponding clustering area of each target region was selected as seed point of the target region in the thesis, and then calculated 3×3 neighborhood characteristic mean value of the seed point as the seed point’s initial value, thus properly avoiding the influence of seed point selection error and the noise. If the centroid is not an integer, round to integers, and the seed point of clustering region r can be defined as follows:
( ⎡⎣∑ Mr
( x ，y
) =
⎤ ，⎡∑ y ⎦ ⎣ Mr
x
N
r0 r0 i r =i 1=i 1
i
N
r
⎤ ⎦
)
r=1，L ，； 4
(6)
Where M r is the pixel number of clustering r, xi and yi are the coordinate value in the image of pixel i of the region (taking upper left corner as the origin of coordinate, and downwards as X axis, the right side as Y axis ) .
3315
Wu-jian / Procedia – 3316 Wu-jian/ ProcediaEngineering Engineering29 00(2012) (2011)3312 000–000
4
3.2. Regional Growth In the process of regional growth, it is necessary to compare the distance of pixel (i,j) to the mean value of color and texture in a certain area, according to equation (2.1) and (2.2), distances can be defined as follows: 2 2 2 ] + w2 [ S (i, j ) − S avg ] + w3 [V (i, j ) − Vavg ] ⎤⎦ ⎣ [ DGra (i= , j ; avg ) HH (i，j ) − HH avg
DCol (i , j ; = avg ) ⎡ w1 H (i , j ) − H avg
1/ 2
(7) (8)
Where H (i , j ) 、 S (i , j ) and V (i , j ) are separately H 、 S and V components of point(i,j) after normalization, H avg 、 S avg and V avg are the H 、 S 、 V mean value of pixels that have been added to the growing region, HH (i，j ) is the information entropy of point(i,j) in the image, HH avg is the mean value of information entropy of pixels that have added to the growing region, w1 、 w2 and w3 are three weight coefficients. According to the equation (9) and (10)，weighted distance can be defined as follows:： = DF (i , j ; avg ) w4 DCol (i , j ; avg ) + w5 DGra (i , j ; avg ) (9) Where, w4 、 w5 are weight coefficients. Based on the definition of distance above, the region growing rule is defined as follows: DF (i , j ; avg ) < T1
(10)
And T1 is the threshold condition of the region segmentation. Then the seed point calculated above can be used to do the regional growth. 4. Process after Segmentation Results
Making morphology processing and holes filling to the segmentation result got through the regional growth algorithm can make the edge of segmentation result more smoothing, and this process is significant to the image segmentation. To the segmentation result of the target area 2 and 3, one time hole filling was made firstly, and then a square structural element with length of side 3 is used to do an open operation to the segmentation result, and then a closing operation was made, at last, the second time hole filling operation to the segmentation result was made to fill the small meaningless holes. It is worth notice that if the target area does not have a single simply connected region, it is unreasonable to make hole filling to the segmentation result of target area. Figure 2 is the segmentation result after the morphology processing and hole filling above.
Fig. 2. (a) Morphology processing result (binary image); (b) Morphology processing result (color image)
From the figure above, the segmentation result after processing reaches the expected effect.
3316
/ Procedia Engineering (2012) 3312 – 3316 Author Wu-jian name / Procedia Engineering 0029 (2011) 000–000
5. Experimental Results and Analysis
On the basis of the segmentation algorithm above, segmentation experiment was made on two interested areas of color remote sensing image. The size of the first piece of the original remote sensing image is 450 × 266, and 4 seed point positions were identified through K-mean clustering algorithm respectively. According to the regional growing rules, the segmentation result was finally achieved through configuring different threshold condition firstly, and then doing mathematical morphology processing and edge segmentation to the result from the last step. Segmentations result is shown in figure 3 and figure 4, as follows:
Fig. 3. (a) Target region segmentation result; (b) Corresponding contour line of segmentation result
Fig. 4. (a) Target region segmentation result; (b) Corresponding contour line of segmentation result
6. Conclusion
Color remote sensing image is the main study object in the thesis, and a color image segmentation combining K-mean clustering algorithm with regional growth algorithm is put forward, which can be used to segment multi-areas, and the effect of segmentation is satisfying. But there are still some defects to be improved. Reference [1] LI Gang, JIN Rong, ZHU Lei, WANG Lin-lin. Color remote sensing image edge detection algorithm based on multilateral fuzzy mathematical morphology . Microelectronics and Computer. 2009, 26(8). [2] GANG SA Lei-si. Digital image processing (2th edition). BeiJing: Electronic Industry Press, 2003. 306-420. [3] HAN Jin-yu, WANG Shou-zhi. Color image denoising algorithm based on noise character and vector median filter . Computer Application. 2009, 29(9). [4] ZHANG Hong-lin. Proficiency in the typical algorithm and realization of Visual C++ digital image processing. BeiJing: Posts & Telecom Press, 2008.
5