ZHANG Jing, CHEN Lu, ZHUO Li, et al., “Multiple Saliency Features Based Automatic Road Extraction from High-Resolution Multispectral Satellite Images,” Chinese Journal of Electronics, vol. 27, no. 1, pp. 133-139, 2018, doi: 10.1049/cje.2017.11.008
Citation: ZHANG Jing, CHEN Lu, ZHUO Li, et al., “Multiple Saliency Features Based Automatic Road Extraction from High-Resolution Multispectral Satellite Images,” Chinese Journal of Electronics, vol. 27, no. 1, pp. 133-139, 2018, doi: 10.1049/cje.2017.11.008

Multiple Saliency Features Based Automatic Road Extraction from High-Resolution Multispectral Satellite Images

doi: 10.1049/cje.2017.11.008
Funds:  This work is supported by the National Natural Science Foundation of China (No.61370189, No.61531006, No.61372149, and No.61471013), the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (No.CIT&TCD20150311), Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality.
  • Received Date: 2015-12-04
  • Rev Recd Date: 2016-12-04
  • Publish Date: 2018-01-10
  • Roads as important artificial objects are the main body of modern traffic system, which provide many conveniences for human civilization. With the development of remote sensing and hyperspectral imaging technology, how to automatically and accurately extract road network from high-resolution multispectral satellite images has become a hot and challenging research topic of geographic information technology. In this paper, an automatic road extraction method from high-resolution multispectral satellite images is proposed by using multiple saliency features. Firstly, road edge is extracted by detecting local linear edge with Singular value decomposition (SVD). Secondly, road regions are constructed by K-means clustering after extracting the feature of background difference. Then road network is achieved by integrating multiple saliency features with Total variation (TV) based image fusion algorithm. Finally, the non-road parts and noises are removed from road network by optimizing multiple salient features with post-processing and morphological operations. The experimental results show that the proposed method can achieve a superior performance in completeness and correctness.
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  • H. Shi and X. Zhao, "Ground moving target indication for single SAR imagery based on sparse representation", Chinese Journal of Electronics, Vol.43, No.3, pp.431-439, 2015.
    T.T. Mirnalinee, S. Das and K. Varghese, "Integration of region and edge-based information for efficient road extraction from high resolution satellite imagery", Proceeding of International Conference on Advances in Pattern Recognition, Kolkata, Indian, pp.373-376, 2009.
    Y.S. Bae, W.H. Lee and Y.J. Choi, "Automatic road extraction from remote sensing images based on a normalized second derivative map", IEEE Geosciences and Remote Sensing Letters, Vol.12, No.9, pp.1858-1862, 2015.
    M. Revathi and M. Sharmila, "Automatic road extraction using high resolution satellite images based on level set and mean shift methods", International Conference on Electronics Computer Technology, Vol.2, pp.1-7, 2013.
    O. Tuncer, "Fully automatic road network extraction from satellite images", Proc. Recent Adv. in Space Technol, Islamabad, Pakistan, pp.708-714, 2007.
    M. Rohit, P.R. Gupta and A.S. Shukla, "Road extraction using K-means clustering and morphological operations", Image Information Processing, Shimla, Himachal Pradesh, India, pp.1-6, 2011.
    S. Das, T.T. Mirnalinee and K. Varghese, "Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images", IEEE Transactions on Geosciences and Remote Sensing, Vol.49, No.10, pp.3906-3931, 2011.
    X.M. Wang, H.R. Zhao and Z.S. Tang, "Road extraction in remote sensing images based on PCNN and mathematical morphology", Proceedings of SPIE-The International Society for Optical Engineering, Vol.7455, No.74550N, 2009.
    C. Luc, Lefevre and Sebastien, "Road network extraction from remote sensing using region-based mathematical morphology", International Workshop on Pattern Recognition in Remote Sensing, pp.3906-3931, 2011.
    L. Itti, C. Koch and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.20, No.11, pp.1254-1259, 1998.
    R.S. Li and W. Cao, "Road network extraction method from remote sensing images based on saliency", Computer Systems and Applications, Vol.23, No.8, pp.114-118, 2014.
    C. Wang, J. Zhang and W.H. Geng, "Visual saliency based automatic road extraction from high-resolution multispectral satellite images", International Conference on Internet Multimedia Computing and Service, Zhangjiajie city, Hunan, China, pp.367-377, 2015.
    Y. Zhou, "Adaptive K-means clustering for color image segmentation", Advances in Information Sciences and Service Sciences, Vol.3, No.10, pp.216-223, 2011.
    M. Kumar, "Total variation regularization-based adaptive pixel level image fusion", IEEE Workshop on Signal Processing Systems, pp.25-30, 2010.
    I.L. Rudin and S. Osher, "Nonlinear total variation based noise removal algorithms", Physic D, Vol.60, No.1-4, pp.259-268, 1992.
    P. Martin, "Fast connected component labeling in binary images", Telecommunications and Signal Processing, Vol.131, No.5, pp.706-709, 2012.
    P.H. Zou, "A noise reduction algorithm for binary image based on mathematical morphology", Control & Automation, Vol.26, No.32, pp.202-204, 2010.
    W. Zhang, D. Shi and X. Yang, "An improved edge detection algorithm based on mathematical morphology and directional wavelet transform", International Congress on Image and Signal Processing, Shenyang, China, 2015.
    T.Y. Zhang and C.Y. Suen, "A fast parallel thinning algorithm for thinning digital patterns", Communication of ACM, Vol.27, No.3, pp.236-239, 1984.
    C. Heipke, H. Mayer and C. Wiedemann, "Evaluation of automatic road extraction", In Proc. Int. Arch. Photogramm. Remote Sense, pp.47-56, 1997.
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