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13 March 1996 Feature extraction of clouds from GOES satellite data for integrated model measurement visualization
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Proceedings Volume 2666, Image and Video Processing IV; (1996) https://doi.org/10.1117/12.234744
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
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 modeled 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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Craig M. Wittenbrink, Glen G. Langdon Jr., and Gabriel Fernandez "Feature extraction of clouds from GOES satellite data for integrated model measurement visualization", Proc. SPIE 2666, Image and Video Processing IV, (13 March 1996); https://doi.org/10.1117/12.234744
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