Polar cloud and surface classification using AVHRR imagery - An intercomparison of methods

• Main Authors: Welch, R. M., Sengupta, S. K., Goroch, A. K., Rabindra, P., Rangaraj, N., Navar, M. S.
• Format: Other/Unknown Material
• Language: unknown
• Published: 1992
• Subjects: 42; Arctic
• Online Access: http://ntrs.nasa.gov/search.jsp?R=19920055458

Six Advanced Very High-Resolution Radiometer local area coverage (AVHRR LAC) arctic scenes are classified into ten classes. Three different classifiers are examined: (1) the traditional stepwise discriminant analysis (SDA) method; (2) the feed-forward back-propagation (FFBP) neural network; and (3) the probabilistic neural network (PNN). More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6 percent, 87.6 percent, and 87.0 percent for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1 percent.