Brightness Transformation and CNN-MRF Model for Road Network Extraction using RSI
DOI:
https://doi.org/10.24113/ijoscience.v6i2.267Keywords:
Remote Sensing Images, Road Extraction, Image Processing, Image Enhancement, CNN, MRF ModelAbstract
For effective urban planning and GIS database, it is necessary to extract effectively the network of road from remote sensing images. The very high spatial resolution images (VHR) taken by space and space probes are the main source of an accurate extraction of the route. Manual techniques disappear because they take time and are expensive. As a result, the much more automated route extraction method has become a research tool in the processing of remote sensing information. The extraction of road networks in remote urban areas of images plays an important role in many urban applications (eg. Road traffic, geometric correction of remote sensing images in cities, updating geographical information, etc.). Because of the complex geometry of buildings and the geometry of sensor detection, it is generally difficult to distinguish the road from its background. In this paper, a hybrid method is proposed for the extraction of paths from high resolution images based on the segmentation using sigmoid CNN-MRF model. The proposed method includes noise removal and enhancement using brightness transformation function then segmentation of road and non-road pixels using CNN and edges are joined using CNN model also. And lastly the markov random field is used connecting edges with similar end points. Simulation will be conducted on remote sensing images in urban, suburban and rural areas to demonstrate the proposed method and compare it with other similar approaches. The results show better performance of proposed road network extraction method as compared to existing technique.
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[2] S. Sun, W. Xia, B. Zhang and Y. Zhang, "Road Centerlines Extraction from High Resolution Remote Sensing Image," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 3931-3934.
[3] Y. Li, R. Zhang and Y. Wu, "Road network extraction in high-resolution SAR images based CNN features," 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, 2017, pp. 1664-1667.
[4] W. Xia, N. Zhong, D. Geng and L. Luo, "A weakly supervised road extraction approach via deep convolutional nets based image segmentation," International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, 2017, pp. 1-5.
[5] L. A. N. Yapa and A. S. Atukorale, "Lane detection using hybrid colour segmentation and perpendicular traversal linear search algorithm," 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, 2017, pp. 1-5.
[6] Weixing Wang, Nan Yanga, Yi Zhanga, Fengping Wanga, Ting Cao, Patrik Eklund, “A review of road extraction from remote sensing images”, Journal of Traffic and Transportation Engineering, Volume 3, Issue 3, June 2016, Pages 271-282.
[7] Chuanrong Li, L. Ma, M. Zhou and Xiaoling Zhu, "Study on road detection method from full-waveform LiDAR data in forested area," 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services (UPINLBS), Shanghai, 2016, pp. 234-239.
[8] Naveen Chandra and Jayanta Kumar Ghosh, “A Cognitive Perspective on Road Network Extraction from High Resolution Satellite Images”, International Conference on Next Generation Computing Technologies, IEEE, 2016.
[9] Jianhua Wang, Qiming Qin, Jianghua Zhao, “A knowledge-based method for road damage detection using high-resolution remote sensing image”, Geoscience and Remote Sensing Symposium, IEEE, 2015.
[10] Rizwan Ahmed Ansari and Krishna Mohan Buddhiraju. “Noise filtering of remotely sensed images using hybrid wavelet and curvelet transform approach”, Geoscience and Remote Sensing Symposium (IGARSS), 2015.
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Copyright (c) 2020 Sadaf Jahan, Dr. Abhishek Bhatt
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