Maximum likelihood estimation of cloud height from multi-angle satellite imagery

TL;DR

This paper introduces a likelihood-based method for estimating cloud height from multi-angle satellite images, leveraging super-resolution and probabilistic modeling to improve accuracy and unify data analysis.

Contribution

The paper presents a novel likelihood estimation technique for cloud height that incorporates super-resolution modeling, offering comparable results to existing methods and enabling unified data analysis.

Findings

Achieved cloud height estimates comparable to the M2 stereo matcher.

Demonstrated the potential for unified modeling of MISR data.

Extended framework for fast, global cloud height estimation is underway.

Abstract

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.