Volume 10 Issue 1
Feb.  2021
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2021  >  10(1): 20-34
ZOU Huanxin, LI Meilin, CAO Xu, et al. Superpixel segmentation for PolSAR images based on geodesic distance[J]. Journal of Radars, 2021, 10(1): 20–34. doi: 10.12000/JR20121
Citation: ZOU Huanxin, LI Meilin, CAO Xu, et al. Superpixel segmentation for PolSAR images based on geodesic distance[J]. Journal of Radars, 2021, 10(1): 20–34. doi: 10.12000/JR20121
shu

Superpixel Segmentation for PolSAR Images Based on Geodesic Distance

DOI: 10.12000/JR20121
  • 1.

    College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

  • 2.

    School of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

Funds:  The National Natural Science Foundation of China (62071474, 41601436)
More Information
  • Corresponding author: ZOU Huanxin, hxzou2008@163.com
  • Received Date: 2020-08-31
  • Rev Recd Date: 2020-11-20
    • Available Online: 2020-12-03
  • Publish Date: 2021-02-25
    Abstract
  • Considering the lack of similarity capabilities of the distance metric used in the traditional Polarimetric Synthetic Aperture Radar (PolSAR) image superpixel segmentation algorithm, a novel PolSAR image superpixel segmentation algorithm based on geodesic distance is proposed in this paper. First, the PolSAR image is initialized as a hexagonal distribution, and all pixels are initialized as unstable pixels. Thereafter, the geodesic distance between two real symmetric Kennaugh matrices is used to measure the similarity between the current unstable point and another cluster point in the search region to more accurately assign labels to unstable points, thereby effectively reducing the number of unstable points. Finally, the postprocessing procedure is used to remove small, isolated regions and generate the final superpixels. To verify the effectiveness of the initialization method and the high efficiency of the geodesic distance, extensive experiments are conducted using simulated PolSAR images. Moreover, the proposed algorithm is analyzed and compared with four other algorithms using simulated and real-world images. Experimental results show that the superpixels generated using the proposed method exhibit higher computational efficiency and a more regular shape that can more accurately fit the edges of real objects compared with those using the four other algorithms.

     

    • FullText(HTML)
  • loading
    • References(28)
  • [1]
    邹焕新, 罗天成, 张月, 等. 基于组合条件随机场的极化SAR图像监督地物分类[J]. 雷达学报, 2017, 6(5): 541–553. doi: 10.12000/JR16109

    ZOU Huanxin, LUO Tiancheng, ZHANG Yue, et al. Combined conditional random fields model for supervised PolSAR images classification[J]. Journal of Radars, 2017, 6(5): 541–553. doi: 10.12000/JR16109
    [2]
    邹焕新, 李美霖, 马倩, 等. 一种基于张量积扩散的非监督极化SAR图像地物分类方法[J]. 雷达学报, 2019, 8(4): 436–447. doi: 10.12000/JR19057

    ZOU Huanxin, LI Meilin, MA Qian, et al. An unsupervised PolSAR image classification algorithm based on tensor product graph diffusion[J]. Journal of Radars, 2019, 8(4): 436–447. doi: 10.12000/JR19057
    [3]
    STUTZ D, HERMANS A, and LEIBE B. Superpixels: An evaluation of the state-of-the-art[J]. Computer Vision and Image Understanding, 2018, 166: 1–27. doi: 10.1016/j.cviu.2017.03.007
    [4]
    LI Meilin, ZOU Huanxin, MA Qian, et al. Unsupervised classification of PolSAR image based on tensor product graph diffusion[C]. The Fourth International Workshop on Pattern Recognition, Nanjing, China, 2019: 11198.
    [5]
    TIRANDAZ Z, AKBARIZADEH G, and KAABI H. PolSAR image segmentation based on feature extraction and data compression using weighted neighborhood filter bank and hidden Markov random field-expectation maximization[J]. Measurement, 2020, 153: 107432. doi: 10.1016/j.measurement.2019.107432
    [6]
    SONG Wanying, LI Ming, ZHANG Peng, et al. Superpixel-based hybrid discriminative random field for fast PolSAR image classification[J]. IEEE Access, 2019, 7: 24547–24558. doi: 10.1109/ACCESS.2019.2899116
    [7]
    LIN Huiping, BAO Junliang, YIN Junjun, et al. Superpixel segmentation with boundary constraints for polarimetric SAR images[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018: 6195–6198.
    [8]
    LANG Fengkai, YANG Jie, LI Deren, et al. Mean-shift-based speckle filtering of polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7): 4440–4454. doi: 10.1109/TGRS.2013.2282036
    [9]
    LANG Fengkai, YANG Jie, YAN Shiyong, et al. Superpixel segmentation of polarimetric synthetic aperture radar (SAR) images based on generalized mean shift[J]. Remote Sensing, 2018, 10(10): 1592. doi: 10.3390/rs10101592
    [10]
    LIU Mingyu, TUZEL O, RAMALINGAM S, et al. Entropy rate superpixel segmentation[C]. CVPR 2011, Providence, USA, 2011: 2097–2104.
    [11]
    ZHANG Yuhang, HARTLEY R, MASHFORD J, et al. Superpixels via pseudo-Boolean optimization[C]. 2011 IEEE International Conference on Computer Vision, Barcelona, Spain, 2011: 1387–1394.
    [12]
    LEVINSHTEIN A, STERE A, KUTULAKOS K N, et al. TurboPixels: Fast superpixels using geometric flows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2290–2297. doi: 10.1109/TPAMI.2009.96
    [13]
    NOCK R and NIELSEN F. Statistical region merging[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(11): 1452–1458. doi: 10.1109/TPAMI.2004.110
    [14]
    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): 2274–2282. doi: 10.1109/TPAMI.2012.120
    [15]
    LI Zhengqin and CHEN Jiansheng. Superpixel segmentation using linear spectral clustering[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1356–1363.
    [16]
    ZHU Song, CAO Danhua, JIANG Shixiong, et al. Fast superpixel segmentation by iterative edge refinement[J]. Electronics Letters, 2015, 51(3): 230–232. doi: 10.1049/el.2014.3379
    [17]
    FENG Jilan, CAO Zongjie, and PI Yiming. Polarimetric contextual classification of PolSAR images using sparse representation and superpixels[J]. Remote Sensing, 2014, 6(8): 7158–7181. doi: 10.3390/rs6087158
    [18]
    XIANG Deliang, NI W, ZHANG H, et al. Superpixel segmentation for PolSAR images with local iterative clustering and heterogeneous statistical model[C]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Wuhan, China, 2017: 499–506.
    [19]
    HOU Biao, YANG Chen, REN Bo, et al. Decomposition-feature-iterative-clustering-based superpixel segmentation for PolSAR image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(8): 1239–1243. doi: 10.1109/LGRS.2018.2833492
    [20]
    张月, 邹焕新, 邵宁远, 等. 一种用于极化SAR图像的快速超像素分割算法[J]. 雷达学报, 2017, 6(5): 564–573. doi: 10.12000/JR17018

    ZHANG Yue, ZOU Huanxin, SHAO Ningyuan, et al. Fast superpixel segmentation algorithm for PolSAR images[J]. Journal of Radars, 2017, 6(5): 564–573. doi: 10.12000/JR17018
    [21]
    RATHA D, DE S, CELIK T, et al. Change detection in polarimetric SAR images using a geodesic distance between scattering mechanisms[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(7): 1066–1070. doi: 10.1109/LGRS.2017.2696158
    [22]
    RATHA D, BHATTACHARYA A, and FRERY A C. Unsupervised classification of PolSAR data using a scattering similarity measure derived from a geodesic distance[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(1): 151–155. doi: 10.1109/LGRS.2017.2778749
    [23]
    NAKAHARA M. Geometry, Topology and Physics[M]. 2nd ed. Boca Raton, USA: CRC Press, 2003.
    [24]
    王雪松, 陈思伟. 合成孔径雷达极化成像解译识别技术的进展与展望[J]. 雷达学报, 2020, 9(2): 259–276. doi: 10.12000/JR19109

    WANG Xuesong and CHEN Siwei. Polarimetric synthetic aperture radar interpretation and recognition: Advances and perspectives[J]. Journal of Radars, 2020, 9(2): 259–276. doi: 10.12000/JR19109
    [25]
    YOU Biao, YANG Jian, YIN Junjun, et al. Decomposition of the Kennaugh matrix based on a new norm[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(5): 1000–1004. doi: 10.1109/LGRS.2013.2284336
    [26]
    QIN Fachao, GUO Jiming, and LANG Fengkai. Superpixel segmentation for polarimetric SAR imagery using local iterative clustering[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(1): 13–17. doi: 10.1109/LGRS.2014.2322960
    [27]
    VASILE G, TROUVÉ E, LEE J S, et al. Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(6): 1609–1621. doi: 10.1109/TGRS.2005.864142
    [28]
    QIN Xianxiang, ZOU Huanxin, ZHOU Shilin, et al. Simulation of spatially correlated PolSAR images using inverse transform method[J]. Journal of Applied Remote Sensing, 2015, 9(1): 095082. doi: 10.1117/1.JRS.9.095082
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(2190) PDF downloads(202) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return