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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(3): 225-236
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.

     

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    • 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]
    艾淑芳, 闫钧华, 李大雷, 等. 遥感图像中的机场跑道检测算法[J]. 电光与控制, 2017, 24(2): 43-46. https://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201702009.htm

    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]
    韩萍, 常玲, 程争, 等. 基于h/q分解和贝叶斯迭代分类的跑道检测算法[J]. 系统工程与电子技术, 2016, 38(9): 2048-2054. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201609012.htm

    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]
    黄远程, 宋博文. 形态学重建与Canny结合实现机场跑道边界检测[J]. 遥感信息, 2016, 31(6): 75-82. doi: 10.3969/j.issn.1000-3177.2016.06.012

    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]
    倪维平, 严卫东, 吴俊政, 等. 应用图像方向和宽度谱检测机场跑道[J]. 红外与激光工程, 2014, 43(11): 3655-3662. doi: 10.3969/j.issn.1007-2276.2014.11.026

    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]
    晋瑞锦, 周伟, 杨健. 大场景下的极化SAR机场检测[J]. 清华大学学报(自然科学版), 2014, 54(12): 1588-1593. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201412014.htm

    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]
    卢晓光, 蔺泽山, 韩萍, 等. 自适应无监督分类的PolSAR图像机场跑道区域快速检测[J]. 遥感学报, 2019, 23(6): 1186-1193. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201906016.htm

    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]
    韩萍, 徐建飒, 赵爱军. 基于多级分类的PolSAR图像机场跑道检测[J]. 系统工程与电子技术, 2014, 36(5): 866-871. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201405010.htm

    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]
    张立平, 张红, 王超, 等. 大场景高分辨率SAR图像中机场快速检测方法[J]. 中国图像图形学报, 2010, 15(7): 1112-1120. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201007019.htm

    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]
    陈强, 蒋咏梅, 陆军, 等. 基于目标散射相似性的POLSAR图像无监督地物散射分类新方案[J]. 电子学报, 2010, 38(12): 2729-2734. https://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201012003.htm

    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
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