Volume 8 Issue 4
Aug.  2019
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2019  >  8(4): 479-489
XIAO Dongling and LIU Chang. PolSAR terrain classification based on fine-tuned dilated group-cross convolution neural network[J]. Journal of Radars, 2019, 8(4): 479–489. doi: 10.12000/JR19039
Citation: XIAO Dongling and LIU Chang. PolSAR terrain classification based on fine-tuned dilated group-cross convolution neural network[J]. Journal of Radars, 2019, 8(4): 479–489. doi: 10.12000/JR19039
shu

PolSAR Terrain Classification Based on Fine-tuned Dilated Group-cross Convolution Neural Network

DOI: 10.12000/JR19039

  • Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China

  • University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  The National Natural Science Foundation of China (61471340), The State Key Research Development Program (2017YFB0503001)
More Information
  • Corresponding author: LIU Chang, cliu@mail.ie.ac.cn
  • Received Date: 2019-03-04
  • Rev Recd Date: 2019-03-19
    • Available Online: 2019-05-05
  • Publish Date: 2019-08-28
    Abstract
  • In the study of the terrain classification based on the Polarimetric Synthetic Aperture Radar (PolSAR), the algorithms based on general CNN do not fully utilize the phase information in different channels, and the pixel-by-pixel classification strategy with extensive redundant computation is inefficient. To mitigate these problems, a deep pixel-to-pixel mapping model in the complex domain is used for achieving a fast and accurate PolSAR terrain classification at a low sampling rate. To completely utilize the phase information, this study uses Group-Cross CNN to extend the original model to the complex domain allowing complex-number input signals and significantly improving the classification accuracy. In addition, to speed up the algorithm, a Fine-tuned Dilated Group-Cross CNN (FDGC-CNN) was adopted to directly achieve pixel-to-pixel mapping as well as improve accuracy. We verified the adopted model on two PolSAR images comprising 16 classes terrains from the AIRSAR and 4 classes terrains from the E-SAR. According to our model, the overall classification accuracies were 96.94% and 90.07% respectively while the running time was 4.22 s and 4.02 s respectively. Therefore, FDGC-CNN achieved better accuracy with higher efficiency compared to SVM and traditional CNN.

     

    • FullText(HTML)
  • loading
    • References(29)
  • [1]
    YANG Wen, ZHONG Neng, YANG Xiangli, et al. Rie-mannian sparse coding for classification of PolSAR images[C]. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016: 5698–5701. doi: 10.1109/IGARSS.2016.7730488.
    [2]
    张祥, 邓喀中, 范洪冬, 等. 基于目标分解的极化SAR图像SVM监督分类[J]. 计算机应用研究, 2013, 30(1): 295–298. doi: 10.3969/j.issn.1001-3695.2013.01.076

    ZHANG Xiang, DENG Kazhong, FAN Hongdong, et al. PolSAR SVM supervised classification method combining with polarimetric target decomposition[J]. Application Re-search of Computers, 2013, 30(1): 295–298. doi: 10.3969/j.issn.1001-3695.2013.01.076
    [3]
    ZHANG Hongsheng, ZHANG Yuanzhi, and LIN Hui. Urban land cover mapping using random forest combined with optical and SAR data[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 2012: 6809–6812. doi: 10.1109/IGARSS.2012.6352600.
    [4]
    ZHANG Guangyun and JIA Xiuping. Simplified conditional random fields with class boundary constraint for spec-tral-spatial based remote sensing image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(5): 856–860. doi: 10.1109/LGRS.2012.2186279
    [5]
    ZHANG Zhimian, WANG Haipeng, XU Feng, et al. Complex-valued convolutional neural network and its application in polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12): 7177–7188. doi: 10.1109/TGRS.2017.2743222
    [6]
    徐真, 王宇, 李宁, 等. 一种基于CNN的SAR图像变化检测方法[J]. 雷达学报, 2017, 6(5): 483–491. doi: 10.12000/JR17075

    XU Zhen, WANG R, LI Ning, et al. A novel approach to change detection in SAR images with CNN classification[J]. Journal of Radars, 2017, 6(5): 483–491. doi: 10.12000/JR17075
    [7]
    BI Haixia, SUN Jian, and XU Zongben. A graph-based semisupervised deep learning model for PolSAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4): 2116–2132. doi: 10.1109/TGRS.2018.2871504
    [8]
    CHEN Siwei and TAO Chensong. PolSAR image classification using polarimetric-feature-driven deep convolutional neural network[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(4): 627–631. doi: 10.1109/LGRS.2018.2799877
    [9]
    CHEN Siwei, TAO Chensong, WANG Xuesong, et al. PolSAR target classification using polarimetric-feature-driven deep convolutional neural network[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, 2018. doi: 10.1109/IGARSS.2018.8518529.
    [10]
    KONG J A, SWARTZ A A, YUEH H A, et al. Identification of terrain cover using the optimum polarimetric classifier[J]. Journal of Electromagnetic Waves and Applications, 1988, 2(2): 171–194.
    [11]
    LEE J S, GRUNES M R, and KWOK R. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution[J]. International Journal of Remote Sensing, 1994, 15(11): 2299–2311. doi: 10.1080/01431169408954244
    [12]
    KOUSKOULAS Y, ULABY F T, and PIERCE L E. The Bayesian Hierarchical Classifier (BHC) and its application to short vegetation using multifrequency polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(2): 469–477. doi: 10.1109/TGRS.2003.821066
    [13]
    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
    [14]
    YU F and KOLTUN V. Multi-scale context aggregation by dilated convolutions[J]. arXiv preprint arXiv: 1511.07122, 2015.
    [15]
    LONG J, SHELHAMER E, and DARRELL T. Fully convolutional networks for semantic segmentation[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 3431–3440. doi: 10.1109/CVPR.2015.7298965.
    [16]
    BADRINARAYANAN V, KENDALL A, and CIPOLLA R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation[J]. arXiv preprint ar-Xiv: 1511.00561, 2015.
    [17]
    RONNEBERGER O, FISCHER P, and BROX T. U-net: Convolutional networks for biomedical image segmenta-tion[C]. Proceedings of 18th International Conference on Medical Image Computing and Computer-Assisted Inter-vention, Munich, Germany, 2015: 234–241.
    [18]
    BENGIO Y, LECUN Y, NOHL C, et al. LeRec: A NN/HMM hybrid for on-line handwriting recognition[J]. Neural Computation, 1995, 7(6): 1289–1303. doi: 10.1162/neco.1995.7.6.1289
    [19]
    NAIR V and HINTON G E. Rectified linear units improve restricted boltzmann machines[C]. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel, 2010: 807–814.
    [20]
    SHANG W, SOHN K, ALMEIDA D, et al. Understanding and improving convolutional neural networks via concatenated rectified linear units[C]. International Conference on Machine Learning, 2016: 2217–2225.
    [21]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers: Surpassing human-level per-formance on imagenet classification[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1026–1034. doi: 10.1109/ICCV.2015.123.
    [22]
    SIMONYAN K and ZISSERMAN A. Very deep convolu-tional networks for large-scale image recognition[J]. arXiv preprint arXiv: 1409.1556, 2014.
    [23]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al.. Identity mappings in deep residual networks[C]. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 630–645.
    [24]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
    [25]
    HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2017: 2261–2269. doi: 10.1109/CVPR.2017.243
    [26]
    LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2999–3007. doi: 10.1109.ICCV.2017.324.
    [27]
    HOEKMAN D H and VISSERS M A M. A new polarimetric classification approach evaluated for agricultural crops[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(12): 2881–2889. doi: 10.1109/TGRS.2003.817795
    [28]
    HOU Biao, KOU Hongda, and JIAO Licheng. Classification of polarimetric SAR images using multilayer autoencoders and superpixels[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(7): 3072–3081. doi: 10.1109/JSTARS.2016.2553104
    [29]
    GUO Yanhe, WANG Shuang, GAO Chenqiong, et al. Wishart RBM based DBN for polarimetric synthetic radar data classification[C]. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015: 1841–1844. doi: 10.1109/IGARSS.2015.7326150.
    • 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(3439) PDF downloads(262) 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