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17 June 1996 Automatic neural network-based cloud detection/classification scheme using multispectral and textural features
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In this paper, efficient and robust neural network-based schemes are introduced to perform automatic cloud detection and classification exploiting textural, spectral and temporal features. An unsupervised Kohonen neural network was used to classify the cloud contents of an image into ten different cloud classes. In the first approach, image was segmented into small blocks of size 8 by 8. Inputs to the network consisted of textural features extracted from each block obtained using the wavelet transform (WT). To improve the detection rate and reduce the false positive rate, a multi-channel fusion system was constructed to combine the results of different optical bands. In the second approach, the inputs to the network was a vector consisting of four values of the corresponding pixels in the four bands/channels. In order to keep track of the spectral changes over time, a temporal-based neural network adaptation scheme is also introduced. The simulation results show that the neural network with temporal adaptation can follow the variations of the spectral features and thus achieve high accuracy in cloud detection/classification task at different times. The results using high resolution GOES 8 data show the promise of the Kohonen neural network when used in conjunction with textural and spectral features for cloud detection/classification.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mukhtiar Ahmed Shaikh, Bin Tian, Mahmood R. Azimi-Sadjadi, Kenneth E. Eis, and Thomas H. Vonder Haar "Automatic neural network-based cloud detection/classification scheme using multispectral and textural features", Proc. SPIE 2758, Algorithms for Multispectral and Hyperspectral Imagery II, (17 June 1996);

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