Abstract
In this article a method for reconstructing atmospheric cloud surfaces using a stereo camera system is presented. The proposed camera system utilizes fish-eye lenses in a flexible wide baseline camera setup. The entire workflow from the camera calibration to the creation of the 3D point set is discussed, but the focus is mainly on cloud segmentation and on the image processing steps of stereo reconstruction. Speed requirements, geometric limitations, and possible extensions of the presented method are also covered. After evaluating the proposed method on artificial cloud images, this paper concludes with results and discussion of possible applications for such systems.
Highlights
1.1 MotivationThe reconstruction of objects visible on the open sky is an attractive topic for both practical applications related to aerial navigation or detection, and applied scientific fields of meteorology, astronomy, even photogrammetry
We presented a camera setup and proposed a pipeline for reconstructing atmospheric cloud images, addressing the special problems of this application environment
One set of images was using synthetic Random Dot Stereo (RDS) images and another set was using a template of typical cloud images placed in a planar structure at different elevation levels
Summary
The reconstruction of objects visible on the open sky is an attractive topic for both practical applications related to aerial navigation or detection, and applied scientific fields of meteorology, astronomy, even photogrammetry. The first whole sky digital imager was developed in 1984 (Johnson, 1989). This device captured digital images at blue and red wavelengths using a charge injection device (CID). By processing this data, the first automated cloud detection algorithm was developed which could identify each individual pixel as opaque cloud, thin cloud or no cloud. The detectors have changed over the time: from CID devices, to grayscale CCDs, to RGB CCD sensors (Shields, 2013)
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