An automated image analysis system for determining sea-ice concentration and cloud cover from AVHRR images of the Antarctic

The Australian Bureau of Meteorology operates a meteorological centre at the Casey station in East Antarctica. This centre is able to receive Advanced Very High Resolution Radiometer (AVHRR) data directly from the NOAA satellites and the Bureau uses these data for operational and research applicatio...

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Bibliographic Details
Published in:International Journal of Remote Sensing
Main Authors: Williams, RN, Michael, KJ, Pendelbury, S, Crowther, P
Format: Article in Journal/Newspaper
Language:English
Published: The Charlesworth Group 2002
Subjects:
Information and Computing Sciences
Artificial Intelligence and Image Processing
Computer Vision
Antarctic
Casey Station
East Antarctica
The Antarctic
Antarc*
Antarctica
Sea ice
Online Access:https://doi.org/10.1080/01431160010025989
http://ecite.utas.edu.au/25700
Description
Summary:The Australian Bureau of Meteorology operates a meteorological centre at the Casey station in East Antarctica. This centre is able to receive Advanced Very High Resolution Radiometer (AVHRR) data directly from the NOAA satellites and the Bureau uses these data for operational and research applications. Currently the AVHRR images are interpreted manually. However, the ICEMAPPER analysis system is able to automate this process. The system uses one set of rules to identify areas of high cloud, low cloud, open water, land ice and sea ice and another to determine sea-ice concentration. Some of the rules in ICEMAPPER were derived using information obtained from published and unpublished research, augmented by details given by practising meteorologists. Others were created by having an expert image interpreter analyse a set of representative AVHRR images and processing these analyses, using a statistical package, to produce rules which closely duplicate the manual analyses. The system was tested on six AVHRR images of the East Antarctic coastline, acquired late in the 1997/1998 summer season. It successfully identified 85% of pixels, selected from the images using a regular grid, as belonging to one of the five surface classes: high cloud, low cloud, open water, land ice or sea ice.

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