Sea ice classification using synthetic aperture radar
Garcia, Frank W., Jr.
Nystuen, Jeffrey A.
Bourke, Robert H.
MetadataShow full item record
This study employs Synthetic Aperture Radar (SAR) imagery from the Marginal Ice Zone Experiment (MIZEX) 1987 to identify an optimal set of statistical descriptors that accurately classify three types of ice (first-year, multiyear, odden) and open water. Two groups of statistics, univariate and texture, are compared and contrasted with respect to their skill in classifying the ice types and open water. Individual statistical descriptors are subjected to principal component analysis and discriminant analysis. Principal component analysis was of little use in understanding features of each ice and open water group. Discriminant analysis was valuable in identifying which statistics held the most power. When combined, univariate and texture statistics classified the groups with 89.5% accuracy, univariate alone with 86.8% accuracy and texture alone with 75.4% accuracy. Range and inertia were the strongest univariate and texture discriminators with 74.6% and 50.8% accuracy, respectively. Despite the use of a non-calibrated SAR, univariate statistics were able to classify the images with greater accuracy than texture statistics.
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
Showing items related by title, author, creator and subject.
Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement Bangs, Corey F.; Kruse, Fred A.; Olsen, Chris R. (SPIE, 2013);Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture feature data on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were ...
Improving Identification of Area Targets by Integrated Analysis of Hyperspectral Data and Extracted Texture Features Bangs, Corey F. (Monterey, California. Naval Postgraduate School, 2012-09);Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture features on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were used ...
Humphrey, Matthew Donald (Monterey, California. Naval Postgraduate School, 2003-06);Terrain classification is studied here using the tool of texture analysis of high-spatial resolution panchromatic imagery. This study analyzes the impact and effectiveness of texture analysis on terrain classification ...