Volume 48 Issue 1
Jan.  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
CHE Shouquan, LI Tao, BAO Congwang, et al. Research on denoising method of remote sensing image in mining area[J]. Industry and Mine Automation，2022，48(1)：111-116. DOI: 10.13272/j.issn.1671-251x.2021090086
Citation: CHE Shouquan, LI Tao, BAO Congwang, et al. Research on denoising method of remote sensing image in mining area[J]. Industry and Mine Automation，2022，48(1)：111-116. DOI: 10.13272/j.issn.1671-251x.2021090086
shu

Research on denoising method of remote sensing image in mining area

  • School of Mines and Civil Engineering, Liupanshui Normal University, Liupanshui 553000, China

More Information
  • Received Date: September 26, 2021
  • Revised Date: January 12, 2022
  • Available Online: January 18, 2022
  • Published Date: January 19, 2022
    Graphical Abstract
    • Abstract
  • Denoising is an important preprocessing step for the effective application of remote sensing images in mining area. The existing remote sensing image denoising methods based on statistics, domain transformation and learning generally have the problems of excessive smoothing of details and insufficient texture preservation. Based on the good edge-preserving property of guided filtering, an iterative guided filtering method is proposed. The method enhances the edge characteristics extraction effect of remote sensing images by guided mapping of residual information, and iteratively performing guided filtering and hyper-parameter shrinkage. The iterative guided filtering is combined with traditional wavelet soft threshold, non-local mean (NLM) filtering, block matching 3D(BM3D) filtering and other denoising methods, which improves the peak signal-to-noise ratio of the traditional method effectively. Among them, NLM filtering and BM3D filtering have the most obvious effects on improving the denoising performance. The iterative guided filtering and BM3D filtering are fused, and the denoised images are initially obtained through BM3D filtering to obtain residual data. The iterative guided filtering is used to process the residual data. While improving the image denoising effect, the image detail characteristics are well preserved. The iterative guided filtering and BM3D filtering fusion method are used for coal gangue yard identification and landslide area edge recognition in remote sensing images of mining areas, and good results have been achieved.
    • FullText(HTML)
    • References (20)
  • [1]
    王义方, 李新举, 李富强, 等. 基于多时相遥感影像的采煤塌陷区典型扰动轨迹识别−以山东省济宁市典型高潜水位矿区为例[J]. 地质学报,2019,93(增刊1):301-309.

    WANG Yifang, LI Xinju, LI Fuqiang, et al. Identification of typical disturbance trajectory in coal mining subsidence area based on multi-temporal remote sensing images[J]. Acta Geologica Sinica,2019,93(S1):301-309.
    [2]
    杨宏业, 赵银娣, 董霁红. 基于纹理转移的露天矿区遥感图像超分辨率重建[J]. 煤炭学报,2019,44(12):3781-3789.

    YANG Hongye, ZHAO Yindi, DONG Jihong. Remote sensing image super-resolution of open-pit mining area based on texture transfer[J]. Journal of China Coal Society,2019,44(12):3781-3789.
    [3]
    宋国策, 张志. 内蒙古新巴尔虎右旗多金属矿区扬尘风积物遥感监测方法[J]. 国土资源遥感,2020,32(2):46-53.

    SONG Guoce, ZHANG Zhi. Remote sensing monitoring method for dust and wind accumulation in multimetal mining area of Xin Barag Right Banner, Inner Mongolia[J]. Remote Sensing for Land & Resources,2020,32(2):46-53.
    [4]
    周斌, 李雨鸿, 李辑, 等. 岫岩偏岭矿区植被修复生态环境监测评估[J]. 航天返回与遥感,2019,40(3):103-110. DOI: 10.3969/j.issn.1009-8518.2019.03.013

    ZHOU Bin, LI Yuhong, LI Ji, et al. Monitoring and assessment of vegetation restoration ecology environment in Xiuyan pianling-mining area[J]. Spacecraft Recovery & Remote Sensing,2019,40(3):103-110. DOI: 10.3969/j.issn.1009-8518.2019.03.013
    [5]
    汤伏全, 李林宽, 李小涛, 等. 基于无人机影像的采动地表裂缝特征研究[J]. 煤炭科学技术,2020,48(10):130-136.

    TANG Fuquan, LI Linkuan, LI Xiaotao, et al. Research on characteristics of mining-induced surface cracks based on UAV images[J]. Coal Science and Technology,2020,48(10):130-136.
    [6]
    张元军. 基于双边滤波与小波阈值法的矿区遥感图像处理[J]. 金属矿山,2017(9):170-173. DOI: 10.3969/j.issn.1001-1250.2017.09.035

    ZHANG Yuanjun. Remote sensing image processing method of mining area based on bilateral filtering algorithm and wavelet thresholding method[J]. Metal Mine,2017(9):170-173. DOI: 10.3969/j.issn.1001-1250.2017.09.035
    [7]
    FENG Xubin, ZHANG Wuxia, SU Xiuqin, et al. Optical remote sensing image denoising and super-resolution reconstructing using optimized generative network in wavelet transform domain[J]. Remote Sensing,2021,13(9):1858-1880. DOI: 10.3390/rs13091858
    [8]
    WEN Nu, YANG Shizhi, CUI Shengcheng. High resolution remote sensing image denoising based on curvelet-wavelet transform[J]. Journal of Zhejiang University(Engineering Science),2015,49(1):79-86.
    [9]
    王跃跃, 陈蓉, 于丽君, 等. 结合二维EMD与自适应高斯滤波的遥感卫星影像去噪[J]. 测绘通报,2019(2):22-27.

    WANG Yueyue, CHEN Rong, YU Lijun, et al. Denoising from remote sensing satellite image based on two-dimensional EMD and adaptive Gauss filtering[J]. Bulletin of Surveying and Mapping,2019(2):22-27.
    [10]
    王小兵. 融合提升小波阈值与多方向边缘检测的矿区遥感图像去噪[J]. 国土资源遥感,2020,32(4):46-52.

    WANG Xiaobing. Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area[J]. Remote Sensing for Land & Resources,2020,32(4):46-52.
    [11]
    HUANG Zhenghua, ZHANG Yaozong, QIAN Li, et al. Unidirectional variation and deep CNN denoiser priors for simultaneously destriping and denoising optical remote sensing images[J]. International Journal of Remote Sensing,2019(15):5737-5748.
    [12]
    TIAN Chunwei, XU Yong, ZUO Wangmeng. Image denoising using deep CNN with batch renormalization[J]. Neural Networks,2020,121:461-473. DOI: 10.1016/j.neunet.2019.08.022
    [13]
    LIU Jing, XIANG Pengxia, ZHANG Xiaoyan. An improved generative adversarial network for remote sensing image denoising[C]//The 13th International Conference on Digital Image Processing, Singapore, 2021: 11878-11886.
    [14]
    秦振涛, 杨茹. 基于结构性字典学习的毛儿盖遥感图像去噪研究[J]. 遥感技术与应用,2019,34(4):793-798.

    QIN Zhentao, YANG Ru. Remote sensing image of Mao'ergai denoising based on structured dictionary learning[J]. Remote Sensing Technology and Application,2019,34(4):793-798.
    [15]
    马晓乐. 基于稀疏表示的去噪声遥感图像融合算法优化[D]. 北京: 北京交通大学, 2020.

    MA Xiaole. The algorithm optimization for de-noised remote sensing fusion based on sparse representation[D]. Beijing: Beijing Jiaotong University, 2020.
    [16]
    陈曦. 基于深度卷积神经网络的图像去噪[D]. 合肥: 合肥工业大学, 2019.

    CHEN Xi. Image denoising based on deep convolutional neural networks[D]. Hefei: Hefei University of Technology, 2019.
    [17]
    冯旭斌. 基于深度学习的光学遥感图像去噪与超分辨率重建算法研究[D]. 西安: 中国科学院大学(中国科学院西安光学精密机械研究所), 2020.

    FENG Xubin. Research on deep-learning based optical remote sensing image denosing and super-resoution reconstructing algorithm[D]. Xi’an: University of Chinese Academy of Science(Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences), 2020.
    [18]
    HE Kaiming, SUN Jian, TANG Xiaoou. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.
    [19]
    DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing,2007:2080-2095.
    [20]
    刘佳丽. 基于遥感的露天灰岩矿山开采信息提取[D]. 唐山: 华北理工大学, 2018.

    LIU Jiali. The opencast limestone mine information extraction based on remote sensing[D]. Tangshan: North China University of Science and Technology, 2018.
    • Related Articles

  • Related Articles

    [1]QIN Peilin, ZHANG Chuanwei, ZHOU Libing, WANG Jianlong. Research on 3D target detection of unmanned trackless rubber-tyred vehicle in coal mine[J]. Journal of Mine Automation, 2022, 48(2): 35-41. DOI: 10.13272/j.issn.1671-251x.2021110068
    [2]WU Haiyan, WANG Tianlong, ZHANG Xuhui, MAO Qinghua, DU Yuyang, GAO Baizhan, MA Hongwei. Remote monitoring system of shearer based on Quest3D and PLC[J]. Journal of Mine Automation, 2015, 41(11): 14-17. DOI: 10.13272/j.issn.1671-251x.2015.11.004
    [3]LIU Haiyang, WANG Dahu, WANG Jingchong. Design of virtual training system for shearer operation based on Quest3D[J]. Journal of Mine Automation, 2015, 41(4): 102-105. DOI: 10.13272/j.issn.1671-251x.2015.04.026
    [4]WANG Li-li. 3D Implementation of SVG[J]. Journal of Mine Automation, 2012, 38(12): 33-36.
    [5]YAN Jian-wei, WANG Yun-jia, ZHU Yong. Monitoring of Surface Subsidence Based on D-InSAR Technology in Huainan Mining Area[J]. Journal of Mine Automation, 2011, 37(8): 48-51.
    [6]SHU Li-chu. 3D Visualization Platform of Mine Based on 3D GIS[J]. Journal of Mine Automation, 2011, 37(6): 7-11.
    [7]WANG Ji-shui, GU Wei-jie. Research of Hybrid Spatial Index Technology in 3DGIS[J]. Journal of Mine Automation, 2010, 36(12): 62-65.
    [8]GUO Ju. Design of 3D Virtual Mine Based on 3D GIS Technology[J]. Journal of Mine Automation, 2007, 33(5): 1-4.
    [9]YU Xiao-ya. Programming Technnique of Interactive 3-Dimensional Animation Based on Java 3D[J]. Journal of Mine Automation, 2003, 29(6): 57-59.
    [10]YANG Jing, JIANG Shi-wen, ZHU Yin-fe. Model SZJ-D2 Test Bed of Gummed Tube[J]. Journal of Mine Automation, 2002, 28(5): 19-19.

  • Cited by

    Periodical cited type(10)

    1. 胡志峰，吴龙华，陈竹安. 基于自组织迁移算法小波阈值矿区遥感图像去噪研究. 江西科学. 2025(01): 59-64 .
    2. 裴丹，房坤，庆宇东，陈沛. 基于改进YOLOv5的矿山遥感图像去噪方法. 工矿自动化. 2025(03): 148-155 . 本站查看
    3. 李亦珂，王春梅. 基于离散小波域深度残差学习的矿区遥感图像增强算法. 金属矿山. 2024(04): 215-220 .
    4. 徐浩哲，李鑫雨，闫钰亭，王蓁，常远. 基于优化Canny-Ostu算法的爆破块度识别. 有色金属(矿山部分). 2024(03): 50-55 .
    5. 樊培利，王建军，艾薇. 基于生成对抗残差学习的矿山远程监控图像去噪算法. 金属矿山. 2024(05): 286-292 .
    6. 沈丹萍，赵爽. 基于轻量化YOLOX-S与多阈值分割的矿山遥感图像去噪算法. 金属矿山. 2024(09): 175-180 .
    7. 戴文祥，陈雷，闫鹏飞，王利欣，李波，袁鹏喆. 基于三维激光扫描的煤矿巷道形变监测方法. 工矿自动化. 2023(10): 61-67+95 . 本站查看
    8. 樊华，王文旭，李晓阳，陈睿，韩贞辉. 结合非下采样剪切波变换的BM3D去除图像噪声方法研究. 震灾防御技术. 2023(03): 651-663 .
    9. 李清运，车守全，王浪威. 基于字典学习的遥感图像去噪方法研究. 山东工业技术. 2022(04): 77-81 .
    10. 刘晓明，白宗化. 基于滤波方法的遥感图像去噪算法. 中国电子科学研究院学报. 2022(10): 952-958 .

    Other cited types(2)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (157) PDF downloads (21) Cited by(12)
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd. Visits：1172551    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return