Journal of Geodesy and Geoinformation Science ›› 2020, Vol. 3 ›› Issue (3): 115-127.doi: 10.11947/j.JGGS.2020.0311
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DAI Jiguang1(),GU Yue1,JIN Guang2,ZHU Lei3
Received:
2019-11-04
Accepted:
2020-04-14
Online:
2020-09-20
Published:
2020-09-30
About author:
Jiguang DAI (1978—), male, PhD, associate professor, majors in feature extraction of high resolution images. E-mail: daijg03@163.com
Supported by:
DAI Jiguang,GU Yue,JIN Guang,ZHU Lei. Line Segment Optimization Algorithm for High Resolution Optical Remote Sensing Image Based on Geometric and Texture Constraints[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(3): 115-127.
Fig.7(a) shows the GF-2 satellite image of a certain area in Huludao city, Liaoning province. The spatial resolution is 0.8m, the image size is 1500×1500 pixels, and the coverage is suburban area. By directly extracting line segments from images, 7875 line segments can be obtained. After optimization, it becomes 7504 line segments. Among them, the optimized line segment is 150 and the error line segment is 3, and the whole process takes 67.5s. Due to a large number of line segment detection results, in order to clearly show the line segment extraction and optimization results, the local image is enlarged as shown in Fig.7(a), and it can be found that the area is the gate of the cell and there is more linear information. At the same time, it can be seen that the road edge line segment is obviously fractured in the image blurred by the edge. Using the geometric constraint optimization shown in Fig.7(b), it can be seen that the road lines have been connected, but there is a problem of over connection. The most prominent is that the gate of the community has been connected by road lines, and the line segments in the cell also exist over the connection problem. As shown in Fig.7(c), in this paper, texture constraints are introduced on the basis of geometric constraints. It can be seen that the closure of the connected line segments is not satisfied. Therefore, the problem of over connection of two yellow line segments has been dealt with, thus explaining the necessity of texture constraint in this paper, and verifying the reliability of the algorithm of optimizing line segment combined with geometry and texture."
Tab.1
Line segment optimization results by different algorithms"
Programs | The proposed algorithm | The algorithm in Literature [21] | The algorithm in Literature [22] | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Line segment to be optimized/strip | 90 | 100 | 200 | 90 | 100 | 200 | 169 | 264 | 1870 | ||
Error Optimizing Line Segments/strip | 2 | 6 | 10 | 13 | 21 | 40 | 0 | 0 | 0 | ||
Total number of line segments after optimization/strip | 1417 | 4793 | 11964 | 1396 | 4752 | 11879 | 1278 | 2350 | 8280 | ||
Total length of optimized line segment/ megapixel | 2.94 | 8.55 | 31.54 | 2.95 | 8.57 | 31.57 | 2.15 | 4.07 | 17.57 | ||
Average length of optimized line segment/ pixel | 20.75 | 17.84 | 26.36 | 21.13 | 18.03 | 26.58 | 16.82 | 17.66 | 21.22 | ||
Time consuming/s | 13.90 | 33.03 | 314.62 | 2.66 | 10.55 | 55.63 | 31.18 | 143.35 | 856.89 |
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