Volume 20 Issue 3
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(3): 225-236
Previous
HAN Ping, CHENG Zheng, WAN Yi-shuang, HAN Bin-bin, HAN Shao-cheng. A fast detection method of airport runway area based on region segmentation and Wishart classifier[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 225-236. doi: 10.19818/j.cnki.1671-1637.2020.03.021
Citation: HAN Ping, CHENG Zheng, WAN Yi-shuang, HAN Bin-bin, HAN Shao-cheng. A fast detection method of airport runway area based on region segmentation and Wishart classifier[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 225-236. doi: 10.19818/j.cnki.1671-1637.2020.03.021
shu

A fast detection method of airport runway area based on region segmentation and Wishart classifier

doi: 10.19818/j.cnki.1671-1637.2020.03.021
  • 1.

    College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

  • 2.

    Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

  • 3.

    Basic Experiment Center, Civil Aviation University of China, Tianjin 300300, China

Funds:

National Natural Science Foundation of China 61571442

Tianjin Municipal Education Commission Scientific Research Program Projeet 2018KJ246

Special Foundation for Basic Scientific Research of Central Colleges of China 3122018S008

Special Foundation for Basic Scientific Research of Central Colleges of China 3122019110

More Information
  • Author Bio:

    HAN Ping(1966-), female, professor, PhD, hanpingcauc@163.com

  • Corresponding author: CHENG Zheng(1990-), male, experimentalist, postgraduate, chengzhengcauc@163.com
  • Received Date: 2020-01-15
  • Publish Date: 2020-06-25
    Abstract
  • A fast detection method of airport runway area using polarimetric synthetic aperture radar images was proposed based on region segmentation and Wishart classifier. A simple liner iterative clustering algorithm was utilized to partition polarimetric synthetic aperture radar image into many super-pixels, and these super-pixels were regarded as basic units for subsequent classification processing. An optimized distance measurement method was adopted to assign appropriate category labels for the super-pixels, greatly solving the problem of large redundant computation of traditional Wishart distance measurement factor. The polarization scattering characteristics of the pixels in airport runway area were analyzed, and the interest regions were extracted from the classification results using the weak scattering characteristic of airport runway area. The structural characteristics of airport runway were used to select and identify the extracted interest regions, thereby the accurate location of airport runway region was determined. The validity of the proposed algorithm was tested by the measured data from polarimetric synthetic aperture radar, and the detection results were compared with the traditional pixel-based detection results. Experimental result shows that in the large complicated scenes, the algorithm can detect the runway area fast and effectively, and the detected runway has clear outline and relatively complete structure. Using the simple linear iterative clustering algorithm to preprocess the images reduces the complexity of subsequent processing significantly. Based on the experimental data in the Gulf of Mexico, the processing unit number of Wishart classifier is only 1.0% and 2.4% of the numbers of Freeman+Wishart algorithm and FCM+Wishart algorithm, respectively, and the whole detection time is 9.9% and 27.1% of those of Freeman+Wishart algorithm and FCM+Wishart algorithm, respectively. Based on the experimental data in the Big Island, the processing unit number of Wishart classifier is only 1.0% and 2.6% of those of Freeman+Wishart algorithm and FCM+Wishart algorithm, respectively, and the whole detection time is 14.0% and 31.8% of those of Freeman+Wishart algorithm and FCM+Wishart algorithm, respectively. Thus, the real-time performance of the proposed detection method is superior to that of the pixel-based detection method.

     

    • FullText(HTML)
  • loading
    • References(30)
  • [1]
    LYU Wen-tao, DAI Kai-yan, WU Long, et al. Runway detection in SAR images based on fusion sparse representation and semantic spatial matching[J]. IEEE Access, 2018, 6: 27984-27992. doi: 10.1109/ACCESS.2018.2839025
    [2]
    LIU Neng-yuan, CAO Zong-jie, CUI Zong-yong, et al. Multi-layer abstraction saliency for airport detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 9820-9831. doi: 10.1109/TGRS.2019.2929598
    [3]
    AI Shu-fang, YAN Jun-hua, LI Da-lei, et al. An algorithm for detecting the airport runway in remote sensing image[J]. Electronics Optics and Control, 2019, 24(2): 43-46. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201702009.htm
    [4]
    BUDAK Ü, HALICI U, ŞENGÜR A, et al. Efficient airport detection using line segment detector and fisher vector representation[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(8): 1079-1083. doi: 10.1109/LGRS.2016.2565706
    [5]
    LIU Neng-yuan, CUI Zong-yong, CAO Zong-jie, et al. Airport detection in large-scale SAR images via line segment grouping and saliency analysis[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(3): 434-438. doi: 10.1109/LGRS.2018.2792421
    [6]
    CHEN Fen, REN Rui-long, DE VOORDE T V, et al. Fast automatic airport detection in remote sensing images using convolutional neural networks[J]. Remote Sensing, 2018, 10(3): 1-20.
    [7]
    ZHANG Peng, NIU Xin, DOU Yong, et al. Airport detection on optical satellite images using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8): 1183-1187. doi: 10.1109/LGRS.2017.2673118
    [8]
    XU Yue-lei, ZHU Ming-ming, LI Shuai, et al. End-to-end airport detection in remote sensing images combining cascade region proposal networks and multi-threshold detection networks[J]. Remote Sensing, 2018, 10(10): 1-17.
    [9]
    ZHU Dan, WANG Bin, ZHANG Li-ming. Airport target detection in remote sensing images: a new method based on two-way saliency[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 1096-1100. doi: 10.1109/LGRS.2014.2384051
    [10]
    TANG Ge-fu, XIAO Zhi-feng, LIU Qing, et al. A novel airport detection method via line segment classification and texture classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12): 2408-2412. doi: 10.1109/LGRS.2015.2479681
    [11]
    ZHANG Zhe, ZOU Can, HAN Ping, et al. A runway detection method based on classification using optimized polarimetric features and HOG features for PolSAR images[J]. IEEE Access, 2020, 8: 49160-49168. doi: 10.1109/ACCESS.2020.2979737
    [12]
    HAN Ping, CHANG Ling, CHENG Zheng, et al. Runway detection based on h/q decomposition and iterative Bayesian classification[J]. Systems Engineering and Electronics, 2016, 38(9): 2048-2054. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201609012.htm
    [13]
    HUANG Yuan-cheng, SONG Bo-wen. A two step method based on morphology reconstruction and canny operator for runway edge detection[J]. Remote Sensing Information, 2016, 31(6): 75-82. (in Chinese). doi: 10.3969/j.issn.1000-3177.2016.06.012
    [14]
    NI Wei-ping, YAN Wei-dong, WU Jun-zheng, et al. Detection of airport runway based on the orientation and width spectrums of images[J]. Infrared and Laser Engineering, 2014, 43(11): 3655-3662. (in Chinese). doi: 10.3969/j.issn.1007-2276.2014.11.026
    [15]
    HAN Ping, CHENG Zheng, CHANG Ling. Automatic runway detection based on unsupervised classification in polsar image[C]//IEEE. 16th Integrated Communications, Navigation, and Surveillance Conference. New York: IEEE, 2016: 6E3-1-8.
    [16]
    JIN Rui-jin, ZHOU Wei, YANG Jian. Airport automatic detection in large-scale polarimetric SAR image[J]. Journal of Tsinghua University (Science and Technology), 2014, 54(12): 1588-1593. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201412014.htm
    [17]
    LU Xiao-guang, LIN Ze-shan, HAN Ping, et al. Fast detection of airport runways in PolSAR images based on adaptive unsupervised classification[J]. Journal of Remote Sensing, 2019, 23(6): 1186-1193. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201906016.htm
    [18]
    HAN Ping, XU Jian-sa, ZHAO Ai-jun. PolSAR image runways detection based on multi-stage classification[J]. Systems Engineering and Electronics, 2014, 36(5): 866-871. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201405010.htm
    [19]
    ZHANG Li-ping, ZHANG Hong, WANG Chao, et al. A fast method of airport detection in large scale SAR image with high resolution[J]. Journal of Image and Graphics, 2010, 15(7): 1112-1120. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201007019.htm
    [20]
    FREEMAN A, DURDEN S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963-973. doi: 10.1109/36.673687
    [21]
    MAURYA H, CHAUHAN A, PANIGRAHI R K. A fast alternative to three- and four-component scattering models for polarimetric SAR image decomposition[J]. Remote Sensing Letters, 2017, 8(8): 781-790. doi: 10.1080/2150704X.2017.1317930
    [22]
    LEE J S, GRUNES M R, AINSWORTH T L, et al. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2249-2258. doi: 10.1109/36.789621
    [23]
    JIAO Li-cheng, LIU Fang. Wishart deep stacking network for fast PolSAR image classification[J]. IEEE Transactions on Image Processing, 2016, 25(7): 3273-3286. doi: 10.1109/TIP.2016.2567069
    [24]
    CHEN Shi-qiang, GUO Sheng-long, LI Yang, et al. Unsupervised classification for hybrid polarimetric SAR data based on scattering mechanisms and Wishart classifier[J]. Electronics Letters, 2018, 54(23): 1355-1355. doi: 10.1049/el.2018.7086
    [25]
    GADHIYA T, ROY A K. Optimized Wishart network for an efficient classification of multifrequency PolSAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(11): 1720-1724. doi: 10.1109/LGRS.2018.2861081
    [26]
    LIU Chi, LIAO Wen-zhi, LI Heng-chao, et al. Unsupervised classification of multilook polarimetric SAR data using spatially variant Wishart mixture model with double constraints[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 5600-5613.
    [27]
    ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2281. doi: 10.1109/TPAMI.2012.120
    [28]
    CSILLIK O. Fast segmentation and classification of very high resolution remote sensing data using SLIC superpixels[J]. Remote Sensing, 2017, 9(3): 243-261. doi: 10.3390/rs9030243
    [29]
    XU Qiao, CHEN Qi-hao, YANG Shuai, et al. Superpixel-based classification using K distribution and spatial context for polarimetric SAR images[J]. Remote Sensing, 2016, 8(8): 619-640. doi: 10.3390/rs8080619
    [30]
    CHEN Qiang, JIANG Yong-mei, LU Jun, et al. A new scheme of unsupervised terrain classification for PolSAR imagery based on target scattering similarities[J]. Acta Electronica Sinica, 2010, 38(12): 2729-2734. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201012003.htm
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1029) PDF downloads(566) Cited by()
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return