Cloud and shadow detection is an essential prerequisite for further remote sensing processing, whereas edge-precise segmentation remains a challenging issue. In Refined UNet, we considered the aforementioned task and proposed a two-stage pipeline to achieve the edge-precise segmentation. The isolated segmentation regions in Refined UNet, however, bring inferior visualization and should be sufficiently eliminated. Moreover, an end-to-end model is also expected to jointly predict and refine the segmentation results. In this paper, we propose the end-to-end Refined UNet v2 to achieve joint prediction and refinement of cloud and shadow segmentation, which is capable of visually neutralizing redundant segmentation pixels or regions. To this end, we inherit the pipeline of Refine UNet, revisit the bilateral message passing in the inference of conditional random field (CRF), and then develop a novel bilateral strategy derived from the Guided Gaussian filter. Derived from a local linear model of denoising, our v2 can considerably remove isolated segmentation pixels or regions, which is able to yield “cleaner” results. Compared to the high-dimensional Gaussian filter, the Guided Gaussian filter-based message-passing strategy is quite straightforward and easy to implement so that a brute-force implementation can be easily given in GPU frameworks, which is potentially efficient and facilitates embedding. Moreover, we prove that Guided Gaussian filter-based message passing is highly relevant to the Gaussian bilateral term in Dense CRF. Experiments and results demonstrate that our v2 is quantitatively comparable to Refined UNet, but can visually outperform that from the noise-free segmentation perspective. The comparison of time consumption also supports the potential efficiency of our v2.
Refined UNet V2: End-to-End Patch-Wise Network for Noise-Free Cloud and Shadow Segmentation
