Review on nontraditional perspectives of synthetic aperture radar image despeckling

被引:7
|
作者
Singh, Prabhishek [1 ]
Shankar, Achyut [2 ]
Diwakar, Manoj [3 ]
机构
[1] Bennett Univ, Sch Comp Sci Engn & Technol, Greater Noida, Uttar Pradesh, India
[2] Amity Univ Noida, Amity Sch Engn & Technol, Noida, Uttar Pradesh, India
[3] Graph Era deemed be Univ, Dehra Dun, Uttarakhand, India
关键词
SAR image; speckle noise; nontraditional methods; Bayesian techniques; non-Bayesian techniques; METHOD NOISE; TUTORIAL; MODEL;
D O I
10.1117/1.JEI.32.2.021609
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Synthetic aperture radar (SAR) image despeckling is a preprocessing method. SAR images are, by default, noisy in nature. The kind of noise found in SAR images is called speckle noise. The effect of this noise on SAR images is highly adverse. It degrades the quality of the SAR image, resulting in the loss of vital information. Since SAR images are inherently speckled in nature, it costs a lot of information loss. The removal of such noise from the SAR image is mandatory and is the first step. The elimination of speckle noise from SAR images is called SAR image despeckling. There are various traditional and nontraditional methods of SAR image despeckling based on Bayesian and non-Bayesian techniques. The SAR image despeckling methods based on Bayesian techniques are further subdivided into spatial and transform domains. This paper presents a comparative review of nontraditional perspectives on SAR image despeckling. The comparison is made based on methodology, objectives, merits, and demerits. Its focus is to do analysis of all the latest research done in the field of SAR image despeckling using nontraditional methods.
引用
收藏
页数:17
相关论文
共 50 条
  • [21] An Introduction to ENVI Tools for Synthetic Aperture Radar (SAR) Image Despeckling and Quantitative Comparison of Denoising Filters
    Khosravi, Mohammad Reza
    Akbarzadeh, Omid
    Salari, Seyed Reza
    Samadi, Sadegh
    Rostami, Habib
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON POWER, CONTROL, SIGNALS AND INSTRUMENTATION ENGINEERING (ICPCSI), 2017, : 212 - 215
  • [22] Exploring Distributed Scatterers Interferometric Synthetic Aperture Radar Attributes for Synthetic Aperture Radar Image Classification
    Wei, Mingxuan
    Liu, Yuzhou
    Zhu, Chuanhua
    Wang, Chisheng
    [J]. REMOTE SENSING, 2024, 16 (15)
  • [23] Synthetic aperture radar: From signal to image
    Kwak, S
    Lee, Y
    Shin, D
    Park, W
    [J]. ON THE CONVERGENCE OF BIO-INFORMATION-, ENVIRONMENTAL-, ENERGY-, SPACE- AND NANO-TECHNOLOGIES, PTS 1 AND 2, 2005, 277-279 : 799 - 804
  • [24] Supervised classification for synthetic aperture radar image
    Universite de Nice-Sophia Antipolis, Sophia Antipolis, France
    [J]. ICASSP IEEE Int Conf Acoust Speech Signal Process Proc, (3529-3532):
  • [25] Synthetic Aperture Radar image simulation system
    Xing, Qiang
    Li, Zhen
    Chen, Quan
    [J]. SIXTH INTERNATIONAL SYMPOSIUM ON DIGITAL EARTH: MODELS, ALGORITHMS, AND VIRTUAL REALITY, 2010, 7840
  • [26] Multistatic Synthetic Aperture Radar Image Formation
    Krishnan, V.
    Swoboda, J.
    Yarman, C. E.
    Yazici, B.
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2010, 19 (05) : 1290 - 1306
  • [27] Supervised classification for synthetic aperture radar image
    Dupuis, X
    Mathieu, P
    Barlaud, M
    [J]. ICASSP '99: 1999 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, PROCEEDINGS VOLS I-VI, 1999, : 3529 - 3532
  • [28] SYNTHETIC APERTURE RADAR IMAGE BANDWIDTH COMPRESSION
    KASHEF, BG
    TAM, KK
    [J]. PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 1983, 432 : 45 - 53
  • [29] RADAR VIDEO GENERATED FROM SYNTHETIC APERTURE RADAR IMAGE
    Yamaoka, Tomoya
    Suwa, Kei
    Hara, Teruyuki
    Nakano, Yosuke
    [J]. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 6509 - 6512
  • [30] Deep Multi-Scale Recurrent Network for Synthetic Aperture Radar Images Despeckling
    Zhou, Yuanyuan
    Shi, Jun
    Yang, Xiaqing
    Wang, Chen
    Kumar, Durga
    Wei, Shunjun
    Zhang, Xiaoling
    [J]. REMOTE SENSING, 2019, 11 (21)