Floe Size Mapping from Satellite SAR Images and Icewatch Observations in the Beaufort Sea during Autumn 2015

Abstract

An approach for automatic detection of the sea ice type in the MIZ from RADARSAT-2 SAR images with HH polarization and resolution of50 m has been developed and tested. The approach is based on texture analysis using the GLCM (Gray-Level Co-occurrence Matrix) method and several additional functions based on the estimates of the averaged gradient tensor. A machine learning technique (Support Vector Machine, or SVM) is applied to imagery of ice taken for the region of the Beaufort Sea in autumn 2015, with observations of ice type from two ship cruises used as ground truth. It is found that this method shows promise, but the training requires more collocations than is practical at present-specifically, the ubiquitous inhomogeneity of ice presents a challenge for colocation, as it limits the training set.

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Document Details

Document Type
Technical Report
Publication Date
Jul 26, 2019
Accession Number
AD1078812

Entities

People

  • Erick Erick Rogers
  • Gleb G. Panteleev
  • Hui Shen
  • Julia Crout
  • Luc Rainville
  • Max I. Yaremchuk
  • Tamara Townsend

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Backscattering
  • Beaufort Sea
  • Computer Languages
  • Data Sets
  • Detection
  • Dimensionality Reduction
  • Identification
  • Information Science
  • Machine Learning
  • Ocean Waves
  • Pattern Recognition
  • Physics Laboratories
  • Remote Sensing
  • Supervised Machine Learning
  • Synthetic Aperture Radar

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Polar and Arctic Studies

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects