Sky–cloud pix obtained from ground-based totally sky cameras are normally captured the usage of a
fisheye lens with a wide area of view. However, the sky well-knownshows a large dynamic variety in terms of
luminance, extra than a traditional digital camera can seize. It is thus hard to seize the information of an entire scene with
a everyday camera in a unmarried shot. In most cases, the circumsolar area is overexposed, and the regions near the
horizon are underexposed. This renders cloud segmentation for such pics difficult. In this paper, we suggest HDR Cloud
Seg – an effective technique for cloud segmentation using high-dynamic-range (HDR) imaging based totally on multi
exposure fusion. We describe the HDR picture generation manner and launch a brand new database to the network for
benchmarking. Our proposed technique is the first the usage of HDR radiance maps for cloud segmentation and achieves
superb effects and additionally classify the cloud types by the use of KNN classification.
Sky/cloud imaging can be carried out in one-of-a-kind ways. Satellite imagery, and aerial
pictures are popular, in particular, for large-scale surveys; airborne mild detection and ranging (LiDAR)
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