Bian, Bin, authorShaikh, Mukhtiar A., authorAzimi-Sadjadi, Mahmood R., authorVonder Haar, Thomas H., authorReinke, Donald L., authorIEEE, publisher2015-08-032015-08-031999Tian, Bin, et al., A Study of Cloud Classification with Neural Networks Using Spectral and Textural Features, IEEE Transactions on Neural Networks 10, no. 1 (January 1999): 138-151.Tian, Bin, et al., Errata to "A Study of Cloud Classification with Neural Networks Using Spectral and Textural Features", IEEE Transactions on Neural Networks 10, no. 3 (May 1999): 722.http://hdl.handle.net/10217/166549Errata included.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.born digitalarticleseng©1999 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.cloud classificationfeature extractionneural networksText