A physically consistent stochastic model to observe oil spills and strong scatters on SLC SAR images

被引:3
|
作者
Migliaccio, Maurizio [1 ]
Ferrara, Giuseppe [1 ]
Gambardella, Attilio [1 ]
Nunziata, Ferdinando [1 ]
Sorrentino, Antonio [1 ]
机构
[1] Univ Naples Parthenope, Dipartimento Tecnol, I-80133 Naples, Italy
关键词
component; speckle; SAR; sea; generalized K pdf;
D O I
10.1109/IGARSS.2007.4423049
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
A speckle model to characterize low backscatter areas and areas with strong scatterers in marine SLC SAR images is presented. The model allows using high resolution speckled SAR images instead of dealing with multi-look SAR images where, at the expense of a poorer spatial resolution, the speckle is mitigated. The new approach is based on the use of the three parameters of the generalized K probability density function. This speckle model embodies the Rayleigh, the Rice and the K-distribution scattering scenes, which are descriptor of scenes dominated by Bragg scattering, scenes in which a dominant scatter is present and scenes with a non-Gaussian signal statistic, respectively. A large data-set of ERS 1/2 SLC SAR images, provided by the ESA under the Project CIP-2769, is employed. Results show the effectiveness of the approach.
引用
收藏
页码:1322 / 1325
页数:4
相关论文
共 50 条
  • [31] Combined use of SAR images and numerical simulations to identify the source and trajectories of oil spills in coastal environments
    Cervantes-Hernández, Pedro
    Celis-Hernández, Omar
    Ahumada-Sempoal, Miguel A.
    Reyes-Hernández, Cristóbal A.
    Gómez-Ponce, M. Alejandro
    Marine Pollution Bulletin, 2024, 199
  • [32] Estimating Oil-Water Mixing Ratios of Marine Oil Spills From L-Band Fully Polarimetric SAR Images
    Gou, Chunyu
    Zheng, Honglei
    Zhang, Jie
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2023, 20
  • [33] A stochastic model for oil spill detection in marine environment with SAR data
    Parmiggiani, F.
    Alvarez-Hernandez, L. P.
    Moctezuma-Flores, M.
    REMOTE SENSING OF THE OCEAN, SEA ICE, COASTAL WATERS, AND LARGE WATER REGIONS 2018, 2018, 10784
  • [34] Detection of Marine Oil Spills from PlanetScope Images Using DeepLabV3+ Model
    Kang, Jonggu
    Youn, Youjeong
    Kim, Geunah
    Park, Ganghyun
    Choi, Soyeon
    Yang, Chan-Su
    Yi, Jonghyuk
    Lee, Yangwon
    KOREAN JOURNAL OF REMOTE SENSING, 2022, 38 (06) : 1623 - 1631
  • [35] A STOCHASTIC MODEL FOR VERY HIGH RESOLUTION SAR AMPLITUDE IMAGES OF URBAN AREAS
    Di Martino, Gerardo
    Iodice, Antonio
    Riccio, Daniele
    Ruello, Giuseppe
    2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2014, : 942 - 945
  • [36] Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula
    Mera, David
    Cotos, Jose M.
    Varela-Pet, Jose
    Garcia-Pineda, Oscar
    MARINE POLLUTION BULLETIN, 2012, 64 (10) : 2090 - 2096
  • [37] Speckle reduction of SAR images using a physically based Markov random field model and simulated annealing
    Lankoande, O
    Hayat, MM
    Santhanam, B
    Algorithms for Synthetic Aperture Radar Imagery XII, 2005, 5808 : 210 - 221
  • [38] Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007
    Kim, Daeseong
    Jung, Hyung-Sup
    SENSORS, 2018, 18 (07)
  • [39] Comparison of CNNs and Vision Transformers-Based Hybrid Models Using Gradient Profile Loss for Classification of Oil Spills in SAR Images
    Basit, Abdul
    Siddique, Muhammad Adnan
    Bhatti, Muhammad Khurram
    Sarfraz, Muhammad Saquib
    REMOTE SENSING, 2022, 14 (09)
  • [40] Maximum angular multiscale entropy: Characterization of the angular self-similarity patterns in two types of SAR images: Oil spills and low-wind conditions images
    Miranda, Jose Garcia Vivas
    Vasconcelos, Rodrigo Nogueira
    Lentini, Carlos Alessandre Domingos
    Lima, Andre T. Cunha
    Mendonca, Luis Felipe Ferreira
    PHYSICA D-NONLINEAR PHENOMENA, 2023, 455