Survey on Dehazing of Multispectral Images

被引:0
|
作者
Kayalvizhi, S. [1 ]
Karthikeyan, Badrinath [1 ]
Sathvik, Canchibalaji [1 ]
Gautham, Chadalavada [1 ]
机构
[1] Easwari Engn Coll, Dept Comp Sci & Engn, Chennai, India
来源
关键词
Dehazing; Multispectral images; Contrastive learning; CycleGAN; Deep learning; HAZE REMOVAL; SATELLITE DATA;
D O I
10.26713/cma.v14i2.2443
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Images captured in hazy climate can be severely decayed by atmospheric particle scattering, which reduces disparity, and which makes it hard to identify features of an object with the naked human eye. Image defogging is done to get rid of the weather factor effects which in turn improves the image quality of the image. This article gives a brief summary regarding the image denoising techniques that have been proposed. We performed the various approaches in order to find out which is the best method for achieving the perfect result. All methods are analyzed and the corresponding subcategories are presented in accordance with the principles and characteristics. Then, the different methods of quality assessment are described, classified and confidentially discussed. To realize this, we use multispectral sensing. When using filters to divide the wavelengths, multispectral imaging can collect image data in specific wavelength ranges across the spectrum. It can allow for the extraction of additional data that the human eye's visible red, green, and blue color receptors cannot record. Its ideal use was for the identification and reconnaissance of military targets. This system was also used by ISRO to receive high resolution images from their satellite. The proposed method was applied to various types of multispectral images, where its effectiveness for visualizing spectral features was verified.
引用
收藏
页码:1113 / 1125
页数:13
相关论文
共 50 条
  • [1] RSDehazeNet: Dehazing Network With Channel Refinement for Multispectral Remote Sensing Images
    Guo, Jianhua
    Yang, Jingyu
    Yue, Huanjing
    Tan, Hai
    Hou, Chunping
    Li, Kun
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (03): : 2535 - 2549
  • [2] Dehazing for Multispectral Remote Sensing Images Based on a Convolutional Neural Network With the Residual Architecture
    Qin, Manjun
    Xie, Fengying
    Li, Wei
    Shi, Zhenwei
    Zhang, Haopeng
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2018, 11 (05) : 1645 - 1655
  • [3] A survey of classical methods and new trends in pansharpening of multispectral images
    Amro, Israa
    Mateos, Javier
    Vega, Miguel
    Molina, Rafael
    Katsaggelos, Aggelos K.
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2011,
  • [4] A survey of classical methods and new trends in pansharpening of multispectral images
    Israa Amro
    Javier Mateos
    Miguel Vega
    Rafael Molina
    Aggelos K Katsaggelos
    EURASIP Journal on Advances in Signal Processing, 2011
  • [5] Dehazing for images with sun in the sky
    Hu, Fangyu
    Ma, Jie
    Fang, Bin
    Ding, Junfeng
    Zhang, Jun
    JOURNAL OF ELECTRONIC IMAGING, 2019, 28 (04)
  • [6] A Survey on Fusion of Multispectral and Panchromatic Images for High Spatial and Spectral Information
    Sonnad, Shashidhar
    PROCEEDINGS OF THE 2016 IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET), 2016, : 177 - 180
  • [7] A Survey of Image Dehazing Approaches
    Chengtao, C.
    Qiuyu, Z.
    Yanhua, L.
    2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2015, : 3964 - 3969
  • [8] Multispectral Transmission Map Fusion Method and Architecture for Image Dehazing
    Kumar, Rahul
    Kaushik, Brajesh Kumar
    Balasubramanian, R.
    IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2019, 27 (11) : 2693 - 2697
  • [9] Dehazing in hyperspectral images: the GRANHHADA database
    Sol Fernández Carvelo
    Miguel Ángel Martínez Domingo
    Eva M. Valero
    Javier Hernández Andrés
    Scientific Reports, 13
  • [10] A Unified Dehazing Approach for infrared images
    Fang, Tao
    Cao, Zhiguo
    Yan, Ruicheng
    MIPPR 2013: MULTISPECTRAL IMAGE ACQUISITION, PROCESSING, AND ANALYSIS, 2013, 8917