Clouds From Satellite Data

Abstract:

We process multispectral satellite imagery to load into our environmental database on the UCSC/ NPS/MBARI-- REINAS project. We have developed methods for segmenting GOES (Geostationary Operational Environmental Satellite) images that take advantage of the multispectral data available. Our algorithm performs classification of different types of clouds, as well as characterization of the cloud elevations. The resulting information is used to incorporate the texture mapped satellite imagery into a combined model/measurement visualization. The approximate cloud elevations, types, and opacities are used to develop a three-dimensional model of the cloud for use in visualization. Discrete Karhunen-Loeve transformations, or Hotelling transformations, are used to calculate the principle components from the multispectral data. The accurate segmentation and feature extraction of the clouds assists in validation and evaluation of atmospheric prediction models with true remotely sensed data. We demonstrate the integrated measurement model visualization with an Open GL application using texture mapping. The spectral data is also used to control the free parameters in the texture mapping of the modelled clouds. We are working on further improvements to develop novel compression techniques utilizing the KLT with segmentation and feature extraction, and also hope to develop algorithms that visualize the compressed imagery directly.

Paper:

``Feature extraction of clouds from GOES Satellite Data for Integrated Model Measurement Visualization'', Craig M. Wittenbrink, G. Fernandez i Ubiergo and G. Langdon, Jr. In Proceedings of the IS&T/SPIE Symposium on Electronic Imaging: Image and Video Processing IV 1996, Vol. 2666, pages 212-222, R. Stevenson and M. I. Sezan, San Jose, CA 1996.
A pdf version of the paper presented at the SPIE'96 conference on Image and Video Processing can be obtained by clicking here.

Images:

Variety of different views of clouds
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