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September 2009 Maximum likelihood estimation of cloud height from multi-angle satellite imagery
E. Anderes, B. Yu, V. Jovanovic, C. Moroney, M. Garay, A. Braverman, E. Clothiaux
Ann. Appl. Stat. 3(3): 902-921 (September 2009). DOI: 10.1214/09-AOAS243


We develop a new estimation technique for recovering depth-of-field from multiple stereo images. Depth-of-field is estimated by determining the shift in image location resulting from different camera viewpoints. When this shift is not divisible by pixel width, the multiple stereo images can be combined to form a super-resolution image. By modeling this super-resolution image as a realization of a random field, one can view the recovery of depth as a likelihood estimation problem. We apply these modeling techniques to the recovery of cloud height from multiple viewing angles provided by the MISR instrument on the Terra Satellite. Our efforts are focused on a two layer cloud ensemble where both layers are relatively planar, the bottom layer is optically thick and textured, and the top layer is optically thin. Our results demonstrate that with relative ease, we get comparable estimates to the M2 stereo matcher which is the same algorithm used in the current MISR standard product (details can be found in [IEEE Transactions on Geoscience and Remote Sensing 40 (2002) 1547–1559]). Moreover, our techniques provide the possibility of modeling all of the MISR data in a unified way for cloud height estimation. Research is underway to extend this framework for fast, quality global estimates of cloud height.


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E. Anderes. B. Yu. V. Jovanovic. C. Moroney. M. Garay. A. Braverman. E. Clothiaux. "Maximum likelihood estimation of cloud height from multi-angle satellite imagery." Ann. Appl. Stat. 3 (3) 902 - 921, September 2009.


Published: September 2009
First available in Project Euclid: 5 October 2009

zbMATH: 1196.62124
MathSciNet: MR2750219
Digital Object Identifier: 10.1214/09-AOAS243

Keywords: cloud height estimation , depth-of-field , maximum likelihood estimation , multiangle imaging spectroradiometer , Random fields , super resolution

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.3 • No. 3 • September 2009
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