Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network

被引:44
|
作者
Cao, Yice [1 ]
Wu, Yan [1 ]
Zhang, Peng [2 ]
Liang, Wenkai [1 ]
Li, Ming [2 ]
机构
[1] Xidian Univ, Sch Elect Engn, Remote Sensing Image Proc & Fusion Grp, Xian 710071, Shaanxi, Peoples R China
[2] Xidian Univ, Natl Lab Radar Signal Proc, Xian 710071, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
complex-valued deep fully convolutional neural network (CV-FCN); polarimetric synthetic aperture radar (PolSAR) image classification; pixel-level labeling; POLARIMETRIC SAR IMAGERY; NEURAL-NETWORK; SEMANTIC SEGMENTATION; LAND CLASSIFICATION;
D O I
10.3390/rs11222653
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Although complex-valued (CV) neural networks have shown better classification results compared to their real-valued (RV) counterparts for polarimetric synthetic aperture radar (PolSAR) classification, the extension of pixel-level RV networks to the complex domain has not yet thoroughly examined. This paper presents a novel complex-valued deep fully convolutional neural network (CV-FCN) designed for PolSAR image classification. Specifically, CV-FCN uses PolSAR CV data that includes the phase information and uses the deep FCN architecture that performs pixel-level labeling. The CV-FCN architecture is trained in an end-to-end scheme to extract discriminative polarimetric features, and then the entire PolSAR image is classified by the trained CV-FCN. Technically, for the particularity of PolSAR data, a dedicated complex-valued weight initialization scheme is proposed to initialize CV-FCN. It considers the distribution of polarization data to conduct CV-FCN training from scratch in an efficient and fast manner. CV-FCN employs a complex downsampling-then-upsampling scheme to extract dense features. To enrich discriminative information, multi-level CV features that retain more polarization information are extracted via the complex downsampling scheme. Then, a complex upsampling scheme is proposed to predict dense CV labeling. It employs the complex max-unpooling layers to greatly capture more spatial information for better robustness to speckle noise. The complex max-unpooling layers upsample the real and the imaginary parts of complex feature maps based on the max locations maps retained from the complex downsampling scheme. In addition, to achieve faster convergence and obtain more precise classification results, a novel average cross-entropy loss function is derived for CV-FCN optimization. Experiments on real PolSAR datasets demonstrate that CV-FCN achieves better classification performance than other state-of-art methods.
引用
收藏
页数:29
相关论文
共 50 条
  • [1] COMPLEX-VALUED FULLY CONVOLUTIONAL NETWORK FOR POLSAR IMAGE CLASSIFICATION WITH NOISY LABELS
    Wang, Ningwei
    Bi, Haixia
    Wang, Xiaotian
    Chen, Zhao
    [J]. IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 5962 - 5965
  • [2] Deep Triplet Complex-Valued Network for PolSAR Image Classification
    Tan, Xiaofeng
    Li, Ming
    Zhang, Peng
    Wu, Yan
    Song, Wanying
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (12): : 10179 - 10196
  • [3] A Novel Deep Fully Convolutional Network for PolSAR Image Classification
    Li, Yangyang
    Chen, Yanqiao
    Liu, Guangyuan
    Jiao, Licheng
    [J]. REMOTE SENSING, 2018, 10 (12)
  • [4] POLSAR IMAGE CLASSIFICATION VIA COMPLEX-VALUED MULTI-SCALE CONVOLUTIONAL NEURAL NETWORK
    Zhang, Lamei
    Zhang, Siyu
    Dong, Hongwei
    Lu, Da
    [J]. IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 200 - 203
  • [5] A New Architecture of a Complex-Valued Convolutional Neural Network for PolSAR Image Classification
    Ren, Yihui
    Jiang, Wen
    Liu, Ying
    [J]. REMOTE SENSING, 2023, 15 (19)
  • [6] Complex-Valued 3-D Convolutional Neural Network for PolSAR Image Classification
    Tan, Xiaofeng
    Li, Ming
    Zhang, Peng
    Wu, Yan
    Song, Wanying
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2020, 17 (06) : 1022 - 1026
  • [7] Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification
    Xie, Wen
    Jiao, Licheng
    Hua, Wenqiang
    [J]. REMOTE SENSING, 2022, 14 (15)
  • [8] POLSAR IMAGE CLASSIFICATION VIA COMPLEX-VALUED CONVOLUTIONAL NEURAL NETWORK COMBINING MEASURED DATA AND ARTIFICIAL FEATURES
    Qin, Xianxiang
    Hu, Tao
    Zou, Huanxin
    Yu, Wangsheng
    Wang, Peng
    [J]. 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 3209 - 3212
  • [9] PolSAR image classification based on complex-valued convolutional neural network and Markov random field
    Qin, Xianxiang
    Yu, Wangsheng
    Wang, Peng
    Chen, Tianping
    Zou, Huanxin
    [J]. FOURTH INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION, 2019, 11198
  • [10] PolSAR Image Classification Based on Complex-Valued Convolutional Long Short-Term Memory Network
    Fang, Zheng
    Zhang, Gong
    Dai, Qijun
    Xue, Biao
    [J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19