Segmentation of MR and CT Images Using a Hybrid Neural Network Trained by Genetic Algorithms
Neural Processing Letters
Segmentation of ultrasound images by using a hybrid neural network
Pattern Recognition Letters
A Comparison of PCA and GA Selected Features for Cloud Field Classification
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
A robust watermarking system based on SVD compression
Proceedings of the 2006 ACM symposium on Applied computing
An incremental neural network for tissue segmentation in ultrasound images
Computer Methods and Programs in Biomedicine
Tissue segmentation in ultrasound images by using genetic algorithms
Expert Systems with Applications: An International Journal
International Journal of Remote Sensing
Independent Component Analysis for Cloud Screening of Meteosat Images
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Towards a computer-aided diagnosis system for vocal cord diseases
Artificial Intelligence in Medicine
A computationally efficient method for sequential MAP-MRF cloud detection
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
ICA and GA feature extraction and selection for cloud classification
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
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The problem of cloud data classification from satellite imagery using neural networks is considered. 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