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A novel FMH model for road extraction from high-resolution remote sensing images in urban areas
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Procedia Computer Science 00 (2019) 000–000 Available online at www.sciencedirect.com Procedia Computer Science 00 (2019) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 147 (2019) 49–55
2018 International Conference on Identification, Information and Knowledge in the Internet of 2018 International Conference on Identification, Things, IIKIInformation 2018 and Knowledge in the Internet of Things, IIKI 2018
A novel FMH model for road extraction from high-resolution A novel FMH model for road extraction fromareas high-resolution remote sensing images in urban remote sensing images in urban areas Muzhu Honga, Junqi Guoa,*, Yazhu Daia, Zhaoyang Yinb,c Muzhu Honga, Junqi Guoa,*, Yazhu Daia, Zhaoyang Yinb,c
College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China b Southwest Jiaotong University, Chengdu, China College of Information Science and Technology, Beijing Normal611756, University, Beijing, 100875, China b cUniversity of Leeds, Leeds, LS2 9JT, United Kingdom Southwest Jiaotong University, Chengdu, 611756, China c University of Leeds, Leeds, LS2 9JT, United Kingdom
a a
Abstract Abstract With the rapid development of remote sensing satellites and sensors, the higher resolution of remote sensing images can be collected. Comparing with roads of countryside, it is more difficultand to sensors, extract roads of urban areas from remotesensing sensingimages imagescan due various With the rapid development of remote sensing satellites the higher resolution of remote betocollected. interferencewith factors including buildings vehicles. There are regional characteristics withremote specificsensing width and graydue levels of the Comparing roads of countryside, it and is more difficult to extract roads of urban areas from images to various urban road infactors high-resolution thisvehicles. article, aThere novel are model named FMH is proposed to extractwidth the main road levels information interference including images. buildingsInand regional characteristics with specific and gray of the from the remote sensing image in urban areas. After pre-processing of the toimage, c-means is urban roadhigh-resolution in high-resolution images. In this article, a novel model named FMH is proposed extractfuzzy the main road algorithm information applied binary processing, and thus the image theAfter road part and the non-road Then mathematical morphology from thetohigh-resolution remote sensing imageisindivided urban into areas. pre-processing of thepart. image, fuzzy c-means algorithm is is used to binary eliminate more non-road Next the local transform is applied to part. extracting the road regions. Finally applied processing, and thusregions. the image is divided intoHough the road part and the non-road Then mathematical morphology morphological operations used toregions. modify and shapes of roads. This model is tested on the panchromatic image with the is used to eliminate moreare non-road Nextrefine the local Hough transform is applied to extracting the road regions. Finally resolution of 2m collected are by Gaofen-1 satellite, excellent morphological operations used to modify andwhich refineshows shapeseffective of roads.and This model isresults. tested on the panchromatic image with the resolution of 2m collected by Gaofen-1 satellite, which shows effective and excellent results. © 2019 The Author(s). Published by Elsevier B.V. © 2019 The Authors. bybyElsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2019 The Author(s). Published Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the scientific committee of the(https://creativecommons.org/licenses/by-nc-nd/4.0/) 2018 International Conference on Identification, Information and Information and Knowledge in the Internet of Things Peer-reviewinunder responsibility of the scientific committee of the 2018 International Conference on Identification, Knowledge the Internet of Things.
Information and Knowledge in the Internet of Things
Keywords: road extraction; fuzzy c-means; morphology; local Hough transform; Keywords: road extraction; fuzzy c-means; morphology; local Hough transform;
* Corresponding author. Tel:138-1180-5682. address:author. [email protected] * E-mail Corresponding Tel:138-1180-5682. E-mail address: [email protected] 1877-0509 © 2019 The Author(s). Published by Elsevier B.V. This is an open access underPublished the CC BY-NC-ND 1877-0509 © 2019 The article Author(s). by Elsevier license B.V. (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the committee of the(https://creativecommons.org/licenses/by-nc-nd/4.0/) 2018 International Conference on Identification, Information and This is an open access article under thescientific CC BY-NC-ND license Knowledge the Internet of Things Peer-reviewinunder responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Knowledge in the Internet of Things 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Knowledge in the Internet of Things. 10.1016/j.procs.2019.01.183
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Muzhu Hong et al. / Procedia Computer Science 147 (2019) 49–55 Muzhu Hong, Junqi Guo, Yazhu Dai, Zhaoyang Yin / Procedia Computer Science 00 (2019) 000–000
1. Introduction Remote sensing can get information about objects on Earth with non-contact sensors or satellites. With the rapid development of remote sensing satellites and sensors, the higher resolution of remote sensing images can be easily collected. The road is one of the typical man-made ground objects recorded by remote sensing images. Road extraction plays a vital role in both city planning and transportation. The road in high-resolution urban images is mainly characterized by structural rules with intensive distribution. And it is presented as a straight belt with its specific width and gray level in remote sensing images. Recently, theoretical and experimental investigations of road extraction from high-resolution remote sensing images have increased tremendously. Naveen Chandra proposed a cognitive perspective on road network extraction, which could detect the main parts, winding regions and junctions of the roads [1]. Zhen Lv et al introduced a multifeature sparse model to represent the appearance of the target road. A new sparse constraint regularized mean-shift algorithm was used for road tracking, which showed excellent robustness and accuracy [2]. Hairong Ma et al searched for the region of interest by the edge, color and topology features of the road [3]. Besides, both threshold segmentation and mathematical morphology played good roles in Road regions extraction [4, 5, 6]. Fuzzy c-means was also applicable to road extraction [7, 8]. X.Yang and G.Wen used Hough transform to extract the road information from high-resolution images given the fact that most of the city roads are straight [9]. A novel model for road extraction in urban areas is proposed in this article. This model is named FMH for the use of fuzzy c-means, morphology and Hough transform. After the pre-processing of the image [10], fuzzy c-means algorithm is used to divide the image into the road part and the non-road part. Then the erosion operation is conducted on the result above to remove the non-road part. After that the local Hough transform is applied to the sub regions for extracting road features. Finally, the dilation operation and thinning are used to connect broken parts together for better visualized results. The rest of this paper is organized as follows. The proposed model of the road extraction is particularly described in Section 2. The experiment results and conclusion are presented in Section 3 and Section 4 respectively. 2. Proposed Model In this study, a model named FMH is proposed for road extraction from high-resolution remote sensing images in urban areas. The algorithms used in the model are presented below in details. Each step is shown in Fig. 1. 2.1. Fuzzy C-means clustering (FCM) Clustering is an unsupervised classification of patterns. Fuzzy C-means clustering, also known as fuzzy ISODATA, is a common clustering method. The membership function represents the degree of any object x that belongs to set A. And the range of it is between 0 and 1. For each category, all samples can be defined as a specific fuzzy set. FCM algorithm can maximize the similarity of elements in the same category while minimizing the similarity of elements in different categories. The membership function of fuzzy C-means clustering represents the degree of each sample belongs to each class. The algorithm divides n vectors xj (j = 1,2,...,n) into c fuzzy groups. When it minimize the discriminant function, the clustering center ci (i = 1,2,...,c) of each fuzzy group can be obtained [11]. The discriminant function is: c
J (U , c 1 , ..., cc )
Ji
c
n
i 1i 1
j
m
2
u ij d ij
(1)
dij = ||ci - xj|| is the Euclidean distance between the clustering center ci and the data point xj. m is a weighted coefficient which takes the integer more than 1. uij is the membership function of the data point xj belonging to the category i.
Muzhu Hong, Junqi Guo, Yazhu Dai,etZhaoyang Yin /Computer Procedia Computer Science (2019) 000–000 Muzhu Hong al. / Procedia Science 147 (2019)00 49–55
u ij
513
1 c
k 1
d ij d kj
2 / ( m 1)
(2)
The total membership function of xj in each class is equal to 1: c
u ij 1, j 1, ..., n
(3)
i 1
The necessary conditions to minimize formula (1) can be obtained by calculation: n
ci
m
u ij x j
j 1 n
m
u ij
(4)
j 1
The process of the algorithm is iteratively modifying the clustering center and membership function [12]. When the discriminant function is less than the set value or remains unchanged, clustering results are obtained.
Fig. 1 The flow chart of the FMH model
2.2. Mathematical morphology After the fuzzy C-means, the image is divided into two parts: road and non-road. However, the clustering is conducted according to grayscale. A large number of vehicles, roofs and other ground objects also have similar grayscale with the road. Many of them are classified into the road category. So further processing is required to remove
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Muzhu Hong et al. / Procedia Computer Science 147 (2019) 49–55 Muzhu Hong, Junqi Guo, Yazhu Dai, Zhaoyang Yin / Procedia Computer Science 00 (2019) 000–000
non-road regions [13]. Mathematical morphology is adopted because the morphological characteristics of roads are relatively obvious [14]. Mathematical morphology processes images by using the morphological and structural features of image. It has obvious advantages such as simplicity and fast processing speed. Mathematical morphological operators can be used to optimize the original data of images [15]. The function contains maintaining their basic shape characteristics and excluding some irrelevant structures in detail. After proper morphological treatment, road regions will become more complete [16]. The morphological dilation is defined as: ( f b )( s , t )
m a x { f ( s x , t y ) b ( x , y ) | ( s x ), (t y ) D f ; ( x , y ) D b }
(5)
The morphological erosion is defined as: ( f b ) (s, t)
m in { f ( s x , t y ) b ( x , y ) | ( s x ) , ( t y ) D f ; ( x , y ) D b }
(6)
f(x, y) represents a grayscale image, b(x, y) represents the specified structural element, Df and Db represent their definition domain. The road has the characteristics of long length. A certain length of line primitives is used as a structural element to erode the image after clustering. After the extraction of line elements, the shape of the road regions is irregular. There is also a certain degree of fracture. In order to smooth the road regions and connect the road fracture, the line element can be used again to dilate the image. After that, morphological thinning can be achieved through the octave template matching method [17]. Finally, the road information of single pixel width is obtained. 2.3. Local Hough Transform After the original data is processed by fuzzy C-means clustering and mathematical morphological, there are still a large number of non-road regions. Hough Transform is used to extract the line primitives of road regions for the straight line features of the road. The Hough Transform is a classical algorithm extracting lines from digital image [18]. The basic principle of Hough Transform is converting each point in the image dimension to the curve in the parameter space with the duality of points and lines. If some points in the image dimension are collinear, their corresponding curves in the parameter space intersect at a point. Similarly, all the curves intersecting at the same point in the parameter space correspond to the points on a straight line in the image space. In the image space, a line can be represented by the parametric equation (7). The straight line is shown in Fig. 2:
x c o s y s in
(7)
Fig. 2. Polar coordinates of a straight line.
In formula (7), is the distance from the line to the origin in the image, and is the angle between the normal of the straight line and the positive direction of the X-axis. It can be seen from the parametric equation that any point( value and is determined) on the line corresponds
Muzhu Hong et al. / Procedia Computer Science 147 (2019) 49–55 Muzhu Hong, Junqi Guo, Yazhu Dai, Zhaoyang Yin / Procedia Computer Science 00 (2019) 000–000
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to the sinusoidal curve in the parameter space. The points on the same line in the image space are mapped with the intersection point of many sinusoidal curves in the parametric space. It can also be said that an intersection point in the parameter space corresponds to the only straight line in the image space [19]. The core of Hough transform is to achieve the extraction of the image global model through the recognition of local patterns (a point). Compared with other linear detection methods, the advantages of Hough transform are obvious. Hough transform is particularly effective even though the target points on the image are sparse [20]. Hough transform has the characteristics of good anti-noise and anti-fracture capability when processing remote sensing images. However, not all roads penetrate the entire image. If Hough transform is used in the entire image, some non-road points or noise on the same line as a certain road will be detected. Moreover, the threshold needs to be set for the extraction of the lines while the length of roads in the entire image is not same. A smaller threshold value may cause a large number of non-road points mixed in. But if the threshold is too large, only a few longer roads will be detected and the remaining roads will be removed. Therefore, it is difficult to obtain a good result for the whole Hough transform of the image. In this case, the local Hough transform is used to complete the extraction of the road. Firstly, the entire image is divided into some small windows. In order to ensure the integrity of line segments, there are some overlaps of there windows. Then iterative scans are performed one by one and Hough transform is performed on each window according to a threshold. Finally, the results of each window are merged to obtain a road map. In this way, long roads can be detected in each sub-window and the interference of distant collinear noise can also be eliminated. 3. Experimental Result In this section, experiments are carried out to validate the effectiveness of the proposed model. In the following, the experimental data sets and parameters setup are introduced firstly. And then, discussions of the results are provided. 3.1. Experimental data sets and parameters setup There are two original full-color images which were collected by Gaofen-1 satellite with 2m resolution in May 2013 in Jiangsu Province, China. We name them image 1 and image 2. The size of two images is 540*540. In image 1, the road is nearly vertically and horizontally oriented. Other ground objects of this image include a large number of residential buildings and green belts. However, the roads of image 2 are not all horizontal or vertical. Besides of the non-road objects in image 1, there is also a river in the image 2 across from the top to the bottom. Parameters setup is presented as follow. The number of categories c is set to 3 and weighting coefficient m is set to 2 in FCM clustering. In the morphological erosion, a straight line template with length of 3 and width of 1 is used in the direction of both 0 degree and 90 degree. The times of this operation is 5. The size of the sub-window selected in local Hough transform is 270*270 and the threshold is 150. The template in dilation is same as the template in erosion. The times of this operation is 10. 3.2. Discussions of the results Images in Fig. 3 show the process of our model in image1. Fig. 3(a) shows the original full-color image and Fig. 3(b) shows the result of graying and histogram equalization of it. After equalization, the contrast between roads and other ground objects is enhanced. Fig. 3(c) shows the result of image binarization after implementing fuzzy C-means clustering for Fig. 3(b). In the binarized image, the road category is white and the other two categories are black. In addition to the road regions, there are other ground objects that are also classified into road categories. After line erosion and combination of the results in different directions, a large number of non-road regions are removed or weakened, as shown in Fig. 3(d). Local Hough transform is used to extracted long line segment in each window. Fig. 3(e) shows the extracted road regions, in which the non-road regions has been removed. For the results above, the line dilation operation is performed and Fig. 3(f) shows the dilated road. The holes in the road regions are filled, and the broken roads are connected to a certain degree. Finally, morphological thinning is performed in the dilated image. Fig. 3(g) shows the road extraction result with only one pixel width. The result of the extraction is superimposed with the original image as shown in Fig. 3(h).
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Muzhu Hong et al. / Procedia Computer Science 147 (2019) 49–55 Muzhu Hong, Junqi Guo, Yazhu Dai, Zhaoyang Yin / Procedia Computer Science 00 (2019) 000–000
Images in Fig. 4 show our method by processing image 2. Although some sections of roads become curvilinear or fractured. The information of main roads are extracted after the complete process of the image 2. From the processing of two different images, it can be seen that our novel model has a good effect of road extraction.
Fig. 3. Urban road extraction process of image 1. (a) Original image; (b) Result of histogram equalization; (c) Result of fuzzy C-means clustering; (d) Result of Line erosion; (e) Result of local Hough transform; (f) Result of line dilation; (g) Result of thinning; (h) Overlay of extraction result and original image.
Fig. 4. Urban road extraction process of image 2. (a) Original image; (b) Result of histogram equalization; (c) Result of fuzzy C-means clustering; (d) Result of Line erosion; (e) Result of local Hough transform; (f) Result of line dilation; (g) Result of thinning; (h) Overlay of extraction result and original image.
4. Conclusion This paper proposes a novel FMH model for road extraction from high-resolution image in urban areas. The model includes fuzzy C-means, morphology and local Hough transform. After pre-processing, fuzzy C-means clustering is firstly performed using gray features of the road to divide the image into the road regions and the non-road regions. And then, morphological erosion and local Hough transform are performed to remove non-road regions and extract
Muzhu Hong et al. / Procedia Computer Science 147 (2019) 49–55 Muzhu Hong, Junqi Guo, Yazhu Dai, Zhaoyang Yin / Procedia Computer Science 00 (2019) 000–000
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road regions. Finally, morphological dilation and thinning operations are performed to connect broken parts together for better visualized results. Our experiment has successfully proved the validity of this FMH model. Acknowledgement This research is sponsored by Beijing Advanced Innovation Center for Future Education (BJAICFE2016IR-004), “Educational Big Data R&D and its Application”, Major Big Data Engineering Project of National Development and Reform Commission 2017, and National Natural Science Foundation of China (No.61401029). References [1] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [2] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [3] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [4] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [5] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [6] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [7] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [8] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [9] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [10] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [11] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [12] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [13] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [14] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [15] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [16] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [17] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [18] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [19] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [20] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017.
ScienceDirect ScienceDirect
Procedia Computer Science 00 (2019) 000–000 Available online at www.sciencedirect.com Procedia Computer Science 00 (2019) 000–000
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www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 147 (2019) 49–55
2018 International Conference on Identification, Information and Knowledge in the Internet of 2018 International Conference on Identification, Things, IIKIInformation 2018 and Knowledge in the Internet of Things, IIKI 2018
A novel FMH model for road extraction from high-resolution A novel FMH model for road extraction fromareas high-resolution remote sensing images in urban remote sensing images in urban areas Muzhu Honga, Junqi Guoa,*, Yazhu Daia, Zhaoyang Yinb,c Muzhu Honga, Junqi Guoa,*, Yazhu Daia, Zhaoyang Yinb,c
College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China b Southwest Jiaotong University, Chengdu, China College of Information Science and Technology, Beijing Normal611756, University, Beijing, 100875, China b cUniversity of Leeds, Leeds, LS2 9JT, United Kingdom Southwest Jiaotong University, Chengdu, 611756, China c University of Leeds, Leeds, LS2 9JT, United Kingdom
a a
Abstract Abstract With the rapid development of remote sensing satellites and sensors, the higher resolution of remote sensing images can be collected. Comparing with roads of countryside, it is more difficultand to sensors, extract roads of urban areas from remotesensing sensingimages imagescan due various With the rapid development of remote sensing satellites the higher resolution of remote betocollected. interferencewith factors including buildings vehicles. There are regional characteristics withremote specificsensing width and graydue levels of the Comparing roads of countryside, it and is more difficult to extract roads of urban areas from images to various urban road infactors high-resolution thisvehicles. article, aThere novel are model named FMH is proposed to extractwidth the main road levels information interference including images. buildingsInand regional characteristics with specific and gray of the from the remote sensing image in urban areas. After pre-processing of the toimage, c-means is urban roadhigh-resolution in high-resolution images. In this article, a novel model named FMH is proposed extractfuzzy the main road algorithm information applied binary processing, and thus the image theAfter road part and the non-road Then mathematical morphology from thetohigh-resolution remote sensing imageisindivided urban into areas. pre-processing of thepart. image, fuzzy c-means algorithm is is used to binary eliminate more non-road Next the local transform is applied to part. extracting the road regions. Finally applied processing, and thusregions. the image is divided intoHough the road part and the non-road Then mathematical morphology morphological operations used toregions. modify and shapes of roads. This model is tested on the panchromatic image with the is used to eliminate moreare non-road Nextrefine the local Hough transform is applied to extracting the road regions. Finally resolution of 2m collected are by Gaofen-1 satellite, excellent morphological operations used to modify andwhich refineshows shapeseffective of roads.and This model isresults. tested on the panchromatic image with the resolution of 2m collected by Gaofen-1 satellite, which shows effective and excellent results. © 2019 The Author(s). Published by Elsevier B.V. © 2019 The Authors. bybyElsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2019 The Author(s). Published Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the scientific committee of the(https://creativecommons.org/licenses/by-nc-nd/4.0/) 2018 International Conference on Identification, Information and Information and Knowledge in the Internet of Things Peer-reviewinunder responsibility of the scientific committee of the 2018 International Conference on Identification, Knowledge the Internet of Things.
Information and Knowledge in the Internet of Things
Keywords: road extraction; fuzzy c-means; morphology; local Hough transform; Keywords: road extraction; fuzzy c-means; morphology; local Hough transform;
* Corresponding author. Tel:138-1180-5682. address:author. [email protected] * E-mail Corresponding Tel:138-1180-5682. E-mail address: [email protected] 1877-0509 © 2019 The Author(s). Published by Elsevier B.V. This is an open access underPublished the CC BY-NC-ND 1877-0509 © 2019 The article Author(s). by Elsevier license B.V. (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the committee of the(https://creativecommons.org/licenses/by-nc-nd/4.0/) 2018 International Conference on Identification, Information and This is an open access article under thescientific CC BY-NC-ND license Knowledge the Internet of Things Peer-reviewinunder responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Knowledge in the Internet of Things 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Knowledge in the Internet of Things. 10.1016/j.procs.2019.01.183
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Muzhu Hong et al. / Procedia Computer Science 147 (2019) 49–55 Muzhu Hong, Junqi Guo, Yazhu Dai, Zhaoyang Yin / Procedia Computer Science 00 (2019) 000–000
1. Introduction Remote sensing can get information about objects on Earth with non-contact sensors or satellites. With the rapid development of remote sensing satellites and sensors, the higher resolution of remote sensing images can be easily collected. The road is one of the typical man-made ground objects recorded by remote sensing images. Road extraction plays a vital role in both city planning and transportation. The road in high-resolution urban images is mainly characterized by structural rules with intensive distribution. And it is presented as a straight belt with its specific width and gray level in remote sensing images. Recently, theoretical and experimental investigations of road extraction from high-resolution remote sensing images have increased tremendously. Naveen Chandra proposed a cognitive perspective on road network extraction, which could detect the main parts, winding regions and junctions of the roads [1]. Zhen Lv et al introduced a multifeature sparse model to represent the appearance of the target road. A new sparse constraint regularized mean-shift algorithm was used for road tracking, which showed excellent robustness and accuracy [2]. Hairong Ma et al searched for the region of interest by the edge, color and topology features of the road [3]. Besides, both threshold segmentation and mathematical morphology played good roles in Road regions extraction [4, 5, 6]. Fuzzy c-means was also applicable to road extraction [7, 8]. X.Yang and G.Wen used Hough transform to extract the road information from high-resolution images given the fact that most of the city roads are straight [9]. A novel model for road extraction in urban areas is proposed in this article. This model is named FMH for the use of fuzzy c-means, morphology and Hough transform. After the pre-processing of the image [10], fuzzy c-means algorithm is used to divide the image into the road part and the non-road part. Then the erosion operation is conducted on the result above to remove the non-road part. After that the local Hough transform is applied to the sub regions for extracting road features. Finally, the dilation operation and thinning are used to connect broken parts together for better visualized results. The rest of this paper is organized as follows. The proposed model of the road extraction is particularly described in Section 2. The experiment results and conclusion are presented in Section 3 and Section 4 respectively. 2. Proposed Model In this study, a model named FMH is proposed for road extraction from high-resolution remote sensing images in urban areas. The algorithms used in the model are presented below in details. Each step is shown in Fig. 1. 2.1. Fuzzy C-means clustering (FCM) Clustering is an unsupervised classification of patterns. Fuzzy C-means clustering, also known as fuzzy ISODATA, is a common clustering method. The membership function represents the degree of any object x that belongs to set A. And the range of it is between 0 and 1. For each category, all samples can be defined as a specific fuzzy set. FCM algorithm can maximize the similarity of elements in the same category while minimizing the similarity of elements in different categories. The membership function of fuzzy C-means clustering represents the degree of each sample belongs to each class. The algorithm divides n vectors xj (j = 1,2,...,n) into c fuzzy groups. When it minimize the discriminant function, the clustering center ci (i = 1,2,...,c) of each fuzzy group can be obtained [11]. The discriminant function is: c
J (U , c 1 , ..., cc )
Ji
c
n
i 1i 1
j
m
2
u ij d ij
(1)
dij = ||ci - xj|| is the Euclidean distance between the clustering center ci and the data point xj. m is a weighted coefficient which takes the integer more than 1. uij is the membership function of the data point xj belonging to the category i.
Muzhu Hong, Junqi Guo, Yazhu Dai,etZhaoyang Yin /Computer Procedia Computer Science (2019) 000–000 Muzhu Hong al. / Procedia Science 147 (2019)00 49–55
u ij
513
1 c
k 1
d ij d kj
2 / ( m 1)
(2)
The total membership function of xj in each class is equal to 1: c
u ij 1, j 1, ..., n
(3)
i 1
The necessary conditions to minimize formula (1) can be obtained by calculation: n
ci
m
u ij x j
j 1 n
m
u ij
(4)
j 1
The process of the algorithm is iteratively modifying the clustering center and membership function [12]. When the discriminant function is less than the set value or remains unchanged, clustering results are obtained.
Fig. 1 The flow chart of the FMH model
2.2. Mathematical morphology After the fuzzy C-means, the image is divided into two parts: road and non-road. However, the clustering is conducted according to grayscale. A large number of vehicles, roofs and other ground objects also have similar grayscale with the road. Many of them are classified into the road category. So further processing is required to remove
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Muzhu Hong et al. / Procedia Computer Science 147 (2019) 49–55 Muzhu Hong, Junqi Guo, Yazhu Dai, Zhaoyang Yin / Procedia Computer Science 00 (2019) 000–000
non-road regions [13]. Mathematical morphology is adopted because the morphological characteristics of roads are relatively obvious [14]. Mathematical morphology processes images by using the morphological and structural features of image. It has obvious advantages such as simplicity and fast processing speed. Mathematical morphological operators can be used to optimize the original data of images [15]. The function contains maintaining their basic shape characteristics and excluding some irrelevant structures in detail. After proper morphological treatment, road regions will become more complete [16]. The morphological dilation is defined as: ( f b )( s , t )
m a x { f ( s x , t y ) b ( x , y ) | ( s x ), (t y ) D f ; ( x , y ) D b }
(5)
The morphological erosion is defined as: ( f b ) (s, t)
m in { f ( s x , t y ) b ( x , y ) | ( s x ) , ( t y ) D f ; ( x , y ) D b }
(6)
f(x, y) represents a grayscale image, b(x, y) represents the specified structural element, Df and Db represent their definition domain. The road has the characteristics of long length. A certain length of line primitives is used as a structural element to erode the image after clustering. After the extraction of line elements, the shape of the road regions is irregular. There is also a certain degree of fracture. In order to smooth the road regions and connect the road fracture, the line element can be used again to dilate the image. After that, morphological thinning can be achieved through the octave template matching method [17]. Finally, the road information of single pixel width is obtained. 2.3. Local Hough Transform After the original data is processed by fuzzy C-means clustering and mathematical morphological, there are still a large number of non-road regions. Hough Transform is used to extract the line primitives of road regions for the straight line features of the road. The Hough Transform is a classical algorithm extracting lines from digital image [18]. The basic principle of Hough Transform is converting each point in the image dimension to the curve in the parameter space with the duality of points and lines. If some points in the image dimension are collinear, their corresponding curves in the parameter space intersect at a point. Similarly, all the curves intersecting at the same point in the parameter space correspond to the points on a straight line in the image space. In the image space, a line can be represented by the parametric equation (7). The straight line is shown in Fig. 2:
x c o s y s in
(7)
Fig. 2. Polar coordinates of a straight line.
In formula (7), is the distance from the line to the origin in the image, and is the angle between the normal of the straight line and the positive direction of the X-axis. It can be seen from the parametric equation that any point( value and is determined) on the line corresponds
Muzhu Hong et al. / Procedia Computer Science 147 (2019) 49–55 Muzhu Hong, Junqi Guo, Yazhu Dai, Zhaoyang Yin / Procedia Computer Science 00 (2019) 000–000
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to the sinusoidal curve in the parameter space. The points on the same line in the image space are mapped with the intersection point of many sinusoidal curves in the parametric space. It can also be said that an intersection point in the parameter space corresponds to the only straight line in the image space [19]. The core of Hough transform is to achieve the extraction of the image global model through the recognition of local patterns (a point). Compared with other linear detection methods, the advantages of Hough transform are obvious. Hough transform is particularly effective even though the target points on the image are sparse [20]. Hough transform has the characteristics of good anti-noise and anti-fracture capability when processing remote sensing images. However, not all roads penetrate the entire image. If Hough transform is used in the entire image, some non-road points or noise on the same line as a certain road will be detected. Moreover, the threshold needs to be set for the extraction of the lines while the length of roads in the entire image is not same. A smaller threshold value may cause a large number of non-road points mixed in. But if the threshold is too large, only a few longer roads will be detected and the remaining roads will be removed. Therefore, it is difficult to obtain a good result for the whole Hough transform of the image. In this case, the local Hough transform is used to complete the extraction of the road. Firstly, the entire image is divided into some small windows. In order to ensure the integrity of line segments, there are some overlaps of there windows. Then iterative scans are performed one by one and Hough transform is performed on each window according to a threshold. Finally, the results of each window are merged to obtain a road map. In this way, long roads can be detected in each sub-window and the interference of distant collinear noise can also be eliminated. 3. Experimental Result In this section, experiments are carried out to validate the effectiveness of the proposed model. In the following, the experimental data sets and parameters setup are introduced firstly. And then, discussions of the results are provided. 3.1. Experimental data sets and parameters setup There are two original full-color images which were collected by Gaofen-1 satellite with 2m resolution in May 2013 in Jiangsu Province, China. We name them image 1 and image 2. The size of two images is 540*540. In image 1, the road is nearly vertically and horizontally oriented. Other ground objects of this image include a large number of residential buildings and green belts. However, the roads of image 2 are not all horizontal or vertical. Besides of the non-road objects in image 1, there is also a river in the image 2 across from the top to the bottom. Parameters setup is presented as follow. The number of categories c is set to 3 and weighting coefficient m is set to 2 in FCM clustering. In the morphological erosion, a straight line template with length of 3 and width of 1 is used in the direction of both 0 degree and 90 degree. The times of this operation is 5. The size of the sub-window selected in local Hough transform is 270*270 and the threshold is 150. The template in dilation is same as the template in erosion. The times of this operation is 10. 3.2. Discussions of the results Images in Fig. 3 show the process of our model in image1. Fig. 3(a) shows the original full-color image and Fig. 3(b) shows the result of graying and histogram equalization of it. After equalization, the contrast between roads and other ground objects is enhanced. Fig. 3(c) shows the result of image binarization after implementing fuzzy C-means clustering for Fig. 3(b). In the binarized image, the road category is white and the other two categories are black. In addition to the road regions, there are other ground objects that are also classified into road categories. After line erosion and combination of the results in different directions, a large number of non-road regions are removed or weakened, as shown in Fig. 3(d). Local Hough transform is used to extracted long line segment in each window. Fig. 3(e) shows the extracted road regions, in which the non-road regions has been removed. For the results above, the line dilation operation is performed and Fig. 3(f) shows the dilated road. The holes in the road regions are filled, and the broken roads are connected to a certain degree. Finally, morphological thinning is performed in the dilated image. Fig. 3(g) shows the road extraction result with only one pixel width. The result of the extraction is superimposed with the original image as shown in Fig. 3(h).
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Images in Fig. 4 show our method by processing image 2. Although some sections of roads become curvilinear or fractured. The information of main roads are extracted after the complete process of the image 2. From the processing of two different images, it can be seen that our novel model has a good effect of road extraction.
Fig. 3. Urban road extraction process of image 1. (a) Original image; (b) Result of histogram equalization; (c) Result of fuzzy C-means clustering; (d) Result of Line erosion; (e) Result of local Hough transform; (f) Result of line dilation; (g) Result of thinning; (h) Overlay of extraction result and original image.
Fig. 4. Urban road extraction process of image 2. (a) Original image; (b) Result of histogram equalization; (c) Result of fuzzy C-means clustering; (d) Result of Line erosion; (e) Result of local Hough transform; (f) Result of line dilation; (g) Result of thinning; (h) Overlay of extraction result and original image.
4. Conclusion This paper proposes a novel FMH model for road extraction from high-resolution image in urban areas. The model includes fuzzy C-means, morphology and local Hough transform. After pre-processing, fuzzy C-means clustering is firstly performed using gray features of the road to divide the image into the road regions and the non-road regions. And then, morphological erosion and local Hough transform are performed to remove non-road regions and extract
Muzhu Hong et al. / Procedia Computer Science 147 (2019) 49–55 Muzhu Hong, Junqi Guo, Yazhu Dai, Zhaoyang Yin / Procedia Computer Science 00 (2019) 000–000
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road regions. Finally, morphological dilation and thinning operations are performed to connect broken parts together for better visualized results. Our experiment has successfully proved the validity of this FMH model. Acknowledgement This research is sponsored by Beijing Advanced Innovation Center for Future Education (BJAICFE2016IR-004), “Educational Big Data R&D and its Application”, Major Big Data Engineering Project of National Development and Reform Commission 2017, and National Natural Science Foundation of China (No.61401029). References [1] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [2] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [3] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [4] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [5] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [6] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [7] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [8] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [9] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [10] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [11] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [12] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [13] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [14] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [15] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [16] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [17] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [18] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017. [19] N. Chandra and J. K. Ghosh, "A cognitive perspective on road network extraction from high resolution satellite images," 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, 2016, pp. 772-776. [20] Z. Lv, Y. Jia, Q. Zhang and Y. Chen, "An Adaptive Multifeature Sparsity-Based Model for Semiautomatic Road Extraction From HighResolution Satellite Images in Urban Areas," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 8, pp. 1238-1242, Aug. 2017.