Detection and classification of cloud data from geostationary satellite using artificial neural networks

Abstract

This paper presents a neural network-based approach for the detection/classification of cloud field from satellite data in both the visible and infrared (IR) range. Unlike many existing cloud detection schemes which use thresholding and statistical methods, this approach uses singular value decomposition (SVD) to extract image textural features in addition to mean value methodologies. The extracted features are then presented to a self-organizing feature map or Kohonen network for automatic detection and classification of cloud areas. The effectiveness of this method is demonstrated under many situations which are considered difficult for the conventional methods. The proposed method also possesses some interesting classification capabilities which can facilitate future studies on global climatology.<>