Training-free thick cloud removal for Sentinel-2 imagery using value propagation interpolation

作者：Laurens Arp, Holger H. Hoos, Peter M. van Bodegom, Alistair Francis, James Wheeler, Dean van Laar, Mitra Baratchi · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2024 · DOI：10.1016/j.isprsjprs.2024.07.030 · 被引用次数：20 · 研究领域：Advanced Image Fusion Techniques、Image Enhancement Techniques、Remote Sensing in Agriculture

Remote sensing imagery has an ever-increasing impact on important downstream applications, such as vegetation monitoring and climate change modelling. Clouds obscuring parts of the images create a substantial bottleneck in most machine learning tasks that use remote sensing data, and being robust to this issue is an important technical challenge. In many cases, cloudy images cannot be used in a machine learning pipeline, leading to either the removal of the images altogether, or to using suboptimal solutions reliant on recent cloud-free imagery or the availability of pre-trained models for the exact use case. In this work, we propose VPint2, a cloud removal method built upon the VPint algorithm, an easy-to-apply data-driven spatial interpolation method requiring no prior training, to address the problem of cloud removal. This method leverages previously sensed cloud-free images to represent the spatial structure of a region, which is then used to propagate up-to-date information from non-cloudy pixels to cloudy ones. We also created a benchmark dataset called SEN2-MSI-T, composed of 20 scenes with 5 full-sized images each, belonging to five common land cover classes. We used this dataset to evaluate our method against three alternatives: mosaicking, an AutoML-based regression method, and the nearest similar pixel interpolator. Additionally, we compared against two previously published neural network-based methods on SEN2-MSI-T, and evaluate our method on a subset of the popul...