A neural network approach to cloud detection in AVHRR images
Document Type
Conference Proceeding
Date of Original Version
12-1-1991
Abstract
The problem of identifying clouds and fog areas from advanced very high resolution radiometer (AVHRR) images using a neural network approach is used. The backpropagation paradigm was used to train many different architectural configurations of the neural network to classify the cloud content of an 8 × 8-pixel window in an image into five categories (ranging from 100% cloudy to 0% cloudy). The results indicate a large range in the performance of the different architectures. The most successful architectural configuration was used to create cloud masks for a series of AVHRR images. The cloud masks generated compared favorably with a trained analyst and other automated techniques.
Publication Title, e.g., Journal
Proceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks
Citation/Publisher Attribution
Slawinski, Olga, James G. Kowalski, and Peter C. Cornillon. "A neural network approach to cloud detection in AVHRR images." Proceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks (1991). https://digitalcommons.uri.edu/gsofacpubs/998