Towards Edge-Precise Cloud and Shadow Detection on the GaoFen-1 Dataset: A Visual, Comprehensive Investigation

Abstract

Remote sensing images are usually contaminated by opaque cloud and shadow regions when acquired, and therefore cloud and shadow detection arises as one of the essential prerequisites for restoration and prediction of the objects of interest underneath, which are required by further processing and analysis. Cutting-edge, learning-based segmentation techniques, given a well-labeled, sufficient sample set, are significantly developed for such a detection issue and can already achieve region-accurate or even pixel-precise performance. However, it may possibly be problematic to attempt to apply the sophisticated segmentation techniques to label-free datasets in a straightforward way, more specifically, to the remote sensing data generated by the Chinese domestic satellite GaoFen-1. We wish to partially address such a segmentation problem from a practical perspective rather than in a conceptual way. This can be performed by considering a hypothesis that a segmentor, which is sufficiently trained on the well-labeled samples of common bands drawn from a source dataset, can be directly applicable to the custom, band-consistent test cases from a target set. Such a band-consistent hypothesis allows us to present a straightforward solution to the GaoFen-1 segmentation problem by treating the well-labeled Landsat 8 Operational Land Imager dataset as the source and by selecting the fourth, the third, and the second bands, also known as the false-color bands, to construct the band-consistent samples and cases. Furthermore, we attempt to achieve edge-refined segmentation performance on the GaoFen-1 dataset by adopting our prior Refined UNet and v4. We finally verify the effectiveness of the band-consistent hypothesis and the edge-refined approaches by performing a relatively comprehensive investigation, including visual comparisons, ablation experiments regarding bilateral manipulations, explorations of critical hyperparameters within our implementation of the conditional random field, and time consumption in practice. The experiments and corresponding results show that the hypothesis of selecting the false-color bands is effective for cloud and shadow segmentation on the GaoFen-1 data, and that edge-refined segmentation performance of our Refined UNet and v4 can be also achieved.