A NOVEL TWO-STAGE GUIDED FILTERING BASED PANSHARPENING METHOD

被引:0
作者
Zhang, Yiming [1 ]
Li, Xu [1 ]
Gao, Ang [1 ]
Li, Lixin [1 ,2 ]
Yue, Shigang [3 ]
机构
[1] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China
[2] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
[3] Univ Lincoln, Sch Comp Sci, Lincoln LN6 7TS, England
来源
2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2017年
基金
中国国家自然科学基金;
关键词
Pansharpening; guided filter; panchromatic; multispectral; REMOTE-SENSING IMAGES; FUSION; CLASSIFICATION;
D O I
暂无
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Pansharpening methods generally inject the missing spatial details from a high spatial resolution panchromatic (PAN) image into the corresponding co-registered low spatial resolution multispectral (MS) images while preserving the spectral information. However, most of methods extract the details only from PAN image, which may lead to distortions in the sharpened results. Motivated by this, we present a novel two-stage guided filtering based pansharpening method. In the preliminary stage, an injection model based on multi-channel guidance filtering is designed to keep the spectral fidelity of MS imagery. Then a single channel guidance filtering based injection model is proposed to enhance the details in the second stage. The proposed method is tested and verified by GeoEye-1 satellite images. Qualitative and quantitative analyses demonstrate the superiority of the proposed method compared with some state-of-the-art guided filtering based pansharpening methods.
引用
收藏
页码:2609 / 2612
页数:4
相关论文
共 50 条
  • [41] Two-Stage Ultrasound Signal Recognition Method Based on Envelope and Local Similarity Features
    Wang, Liwei
    Lu, Senxiang
    Liu, Xiaoyuan
    Liu, Jinhai
    MACHINES, 2022, 10 (12)
  • [42] Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
    Lee, Hyunseok
    Seo, Jihyun
    Lee, Giwan
    Park, Jongoh
    Yeo, Doyeob
    Hong, Ayoung
    APPLIED SCIENCES-BASEL, 2021, 11 (01): : 1 - 11
  • [43] Hyperspectral pansharpening based on guided filter and Gaussian filter
    Dong, Wenqian
    Xiao, Song
    Li, Yongxu
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2018, 53 : 171 - 179
  • [44] AN UNSUPERVISED CNN-BASED HYPERSPECTRAL PANSHARPENING METHOD
    Guarino, G.
    Ciotola, M.
    Vivone, G.
    Poggi, G.
    Scarpa, G.
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 5982 - 5985
  • [45] Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix
    Qu, Jiahui
    Li, Yunsong
    Du, Qian
    Dong, Wenqian
    Xi, Bobo
    REMOTE SENSING, 2019, 11 (09)
  • [46] Multiple Kernel SVM Based on Two-Stage Learning
    Gong, Xingrui
    Zou, Bin
    Duan, Yuze
    Xu, Jie
    Luo, Qingxin
    Yang, Yan
    IEEE ACCESS, 2020, 8 (101133-101144) : 101133 - 101144
  • [47] A novel automatic two-stage locally regularized classifier construction method using the extreme learning machine
    Du, Dajun
    Li, Kang
    Irwin, George W.
    Deng, Jing
    NEUROCOMPUTING, 2013, 102 : 10 - 22
  • [48] A novel method for multispectral image pansharpening based on high dimensional model representation
    Ozay, Evrim Korkmaz
    Tunga, Burcu
    EXPERT SYSTEMS WITH APPLICATIONS, 2021, 170
  • [49] An Efficient Two-Stage Gene Selection Method for Microarray Data
    Du, Dajun
    Li, Kang
    Deng, Jing
    INTELLIGENT COMPUTING FOR SUSTAINABLE ENERGY AND ENVIRONMENT, 2013, 355 : 424 - 432
  • [50] A sparse representation based pansharpening method
    Yang, Xiaomin
    Jian, Lihua
    Yan, Binyu
    Liu, Kai
    Zhang, Lei
    Liu, Yiguang
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 88 : 385 - 399