Pattern recognition of clouds and ice in polar regions

• Main Authors: Welch, R. M., Sengupta, S. K., Sundar, C. A., Kuo, K. S., Carsey, F. D.
• Format: Other/Unknown Material
• Language: unknown
• Published: 1990
• Subjects: 47; Arctic
• Online Access: http://ntrs.nasa.gov/search.jsp?R=19910051991

The study is based on AVHRR imagery and results from Landsat high-spatial-resolution scenes. Among the textual features investigated are the gray level difference vector (GLDV), and sum and difference histogram (SADH) approaches as well as gray level run length, spatial-coherence, and spectral-histogram measures. The traditional stepwise discriminant analysis and neural-network analysis are used for the identification of 20 Arctic surface and cloud classes. A principal-component analysis and hybrid architecture employing a modularized competitive learning layer are utilized. It is pointed out that the cloud-classification accuracy comparable to that of back-propagation could be achieved with a training time two orders of magnitude faster.