DocumentCode :
1240895
Title :
Operational map-guided classification of SAR sea ice imagery
Author :
Maillard, Philippe ; Clausi, David A. ; Deng, Huawu
Author_Institution :
Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
Volume :
43
Issue :
12
fYear :
2005
Firstpage :
2940
Lastpage :
2951
Abstract :
This paper presents a map-guided sea ice classification system built to work in parallel with the Canadian Ice Service (CIS) operations to produce pixel-based ice maps that complement actual "egg code" maps produced by CIS. The system uses the CIS maps as input to guide classification by providing information on the number of ice types and their final label for specific regions. Segmentation is based on a modified adaptive Markov random field (MRF) model that uses synthetic aperture radar (SAR) intensities and texture features as input. The ice type labeling is performed automatically by gathering evidences based on a priori information on one or two classes and deducing the other labels iteratively by comparing distributions of segments. Three methods for comparing the segment distributions (Fisher criterion, Mahalanobis distance, and Kolmogorov-Smirnov test) were implemented and compared. The system is fully described with special attention to the labeling procedure. Examples are presented in the form of two CIS SAR-based ice maps from the Gulf of Saint Lawrence region and one example from the Beaufort Sea. The results indicate that when the segmentation is good, the labeling attains best results (between 71% and 89%) based on evaluation by a sea ice analyst. Some problems remain to be assessed which are primarily attributable to discrepancies in the information provided by the egg code and what is actually visible in the SAR image. Subscale information on floe size and shape available to human analysts, but not in this classification system, also appear to be a critical information for separating some ice types.
Keywords :
Markov processes; geophysical signal processing; image classification; image segmentation; image texture; oceanographic regions; oceanographic techniques; radar imaging; remote sensing by laser beam; remote sensing by radar; sea ice; synthetic aperture radar; Beaufort Sea; Canadian Ice Service; Fisher criterion; Gulf of Saint Lawrence; Kolmogorov-Smirnov test; Mahalanobis distance; Markov random field; SAR sea ice imagery; gray-level cooccurrence matrix; image classification; image segmentation; image texture; map-guided classification; pixel-based ice maps; sea ice classification; segment distribution comparison; synthetic aperture radar; Computational Intelligence Society; Humans; Image segmentation; Information analysis; Labeling; Markov random fields; Sea ice; Shape; Synthetic aperture radar; Testing; Classification; Fisher; Kolmogorov–Smirnov; Mahalanobis; Markov random field (MRF); distribution comparison; gray-level cooccurrence matrix (GLCM); mapping; sea ice; segmentation; texture;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2005.857897
Filename :
1542365
Link To Document :
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