A study of cloud classification with neural networks using spectral and textural features
Date
1999
Authors
Bian, Bin, author
Shaikh, Mukhtiar A., author
Azimi-Sadjadi, Mahmood R., author
Vonder Haar, Thomas H., author
Reinke, Donald L., author
IEEE, publisher
Journal Title
Journal ISSN
Volume Title
Abstract
The problem of cloud data classification from satellite imagery using neural networks is considered in this paper. Several image transformations such as singular value decomposition(SVD) and wavelet packet (WP) were used to extract the salient spectral and textural features attributed to satellite cloud data in both visible and infrared (IR) channels. In addition, the well-known gray-level cooccurrence matrix (GLCM) method and spectral features were examined for the sake of comparison. Two different neural-network paradigms namely probability neural network (PNN) and unsupervised Kohonen self-organized feature map (SOM) were examined and their performance were also benchmarked on the geostationary operational environmental satellite (GOES) 8 data. Additionally, a postprocessing scheme was developed which utilizes the contextual information in the satellite images to improve the final classification accuracy. Overall, the performance of the PNN when used in conjunction with these feature extraction and postprocessing schemes showed the potential of this neural-network-based cloud classification system.
Description
Errata included.
Rights Access
Subject
cloud classification
feature extraction
neural networks