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Ocean Surface Features Extraction using Morphological Techniques from Sentinel-1A Data

Ameesha P.K, Lekshmi S., Arun Kumar M.N., P.V. Hareesh Kumar

Abstract


SAR (Synthetic Aperture Radar) imagery of the ocean surface obtained from remote sensing
instruments is one of the prominent research areas. In this paper, detection of oceanic
features from SAR using morphological technique is proposed. The detected surface features
are presented in layered form. The main advantage of such representation is features can be
easily enabled or disabled on SAR image. Algorithm is tested on Sentinel-1A dataset of ocean
surface.


Keywords: SAR Images, Surface Features, Layered information, Detection algorithm.


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References


Benjamin Holt, SAR Imaging of the Ocean Surface, SAR Marine User Manual-NOAASAR

C.R Jackson and J.R Apel, Synthetic Aperture Radar Marine User’s Manual

Pranali .A. Hatwar and Dr. Heena R. Kher, Analysis of Speckle Noise Reduction in Synthetic Aperture Radar Images , IJERT Vol 4 Issue 01 January 2015

Nupur Saxena and Neha Rathore , A Review on Speckle Noise Filtering Techniques for SAR images, IJARCSEE Vol 2 Issue 2 February 2013

Edward H. Adelson, Layered Representations for Vision and Video, Dept. Brain and Cognitive Sciences and Media Laboratory Massachusetts Institute of Technology Cambridge, MA 02139

D. Nagarajan, Abitha Gladis N. K, Nagarajan. V, Block Processing And Edge Detection For A Dicom Image

Chen Wang, Alexis Mouche, Pierre Tandeo, Justin Stopa, Nicolas Longepe, Guillaume Erhard, Ralph Foster, Douglas Vande mark, Bertrand Chapron; “A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel-1 wave mode”; HAL Id: hal-02275886,Submitted on 2 Sep 2019

Camilla Brekke and Anne H.S Solberg, “Oil spill detection by satellite remote sensing, Remote sensing of Environment”, November 2004




DOI: https://doi.org/10.37591/.v11i2.967

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