Pattern recognition of clouds and ice in polar regionsThe 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.
Document ID
19910051991
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Welch, R. M. (South Dakota School of Mines and Technology Rapid City, SD, United States)
Sengupta, S. K. (South Dakota School of Mines and Technology Rapid City, SD, United States)
Sundar, C. A. (South Dakota School of Mines and Technology Rapid City, SD, United States)
Kuo, K. S. (South Dakota School of Mines and Technology Rapid City, United States)
Carsey, F. D. (JPL Pasadena, CA, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1990
Subject Category
Meteorology And Climatology
Meeting Information
Meeting: Long-term Monitoring of the Earth''s Radiation Budget