A Comprehensive, Automated Approach to Determining Sea Ice Thickness from SAR Data

Donna Haverkamp, Leen Kiat Soh, Costas Tsatsoulis

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal charges and is not subject to limitations caused by using predefined parameters.

Original languageEnglish (US)
Pages (from-to)46-57
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume33
Issue number1
DOIs
StatePublished - Jan 1995

Fingerprint

Sea ice
ice thickness
sea ice
synthetic aperture radar
heuristics
Ice
expert system
Expert systems
ice
method

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Cite this

A Comprehensive, Automated Approach to Determining Sea Ice Thickness from SAR Data. / Haverkamp, Donna; Kiat Soh, Leen; Tsatsoulis, Costas.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 1, 01.1995, p. 46-57.

Research output: Contribution to journalArticle

@article{d364cfad778a481fa586cb60f6b02c05,
title = "A Comprehensive, Automated Approach to Determining Sea Ice Thickness from SAR Data",
abstract = "This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal charges and is not subject to limitations caused by using predefined parameters.",
author = "Donna Haverkamp and {Kiat Soh}, Leen and Costas Tsatsoulis",
year = "1995",
month = "1",
doi = "10.1109/36.368223",
language = "English (US)",
volume = "33",
pages = "46--57",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

TY - JOUR

T1 - A Comprehensive, Automated Approach to Determining Sea Ice Thickness from SAR Data

AU - Haverkamp, Donna

AU - Kiat Soh, Leen

AU - Tsatsoulis, Costas

PY - 1995/1

Y1 - 1995/1

N2 - This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal charges and is not subject to limitations caused by using predefined parameters.

AB - This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal charges and is not subject to limitations caused by using predefined parameters.

UR - http://www.scopus.com/inward/record.url?scp=0029197306&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029197306&partnerID=8YFLogxK

U2 - 10.1109/36.368223

DO - 10.1109/36.368223

M3 - Article

AN - SCOPUS:0029197306

VL - 33

SP - 46

EP - 57

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 1

ER -