| dc.description.abstract | Cloud detection is an important yet difficult task for optical satellites especially for those images having limited bands in visible to near infrared (VNIR). Some approaches using expert systems that exploit the optical properties of cloud are commonly adopted. However, sufficient spectral bands are still needed to recognize a variety of cloud types for this approach. For high spatial resolution missions, most of them provide only 4 bands in VNIR.
Thanks for the rapid development of computer technique, machine learning (ML) nowadays is another choice for cloud detection. This study aims to build a model based on the Convolutional Neural Networks (CNN), and treat this issue as a binary semantic segmentation problem. Different from traditional computer vision applications on CNN, the huge image size, worse histogram distribution, insufficient data quantity with poor labeling quality, and large variety of cloud formations are the major challenges. Thus, except for accuracy analysis, we focus on the analysis of the relationship between input image size, the selection of normalize method, image preprocess, and the strategy of tone mapping.
In this research, we firstly split multiple satellite imageries and validation datasets into training and testing subsets. To assess the transferability of CNN model, especially for other satellites with higher spatial resolutions but lack of cloud flags, a workflow is designed to first train CNN by Landsat-8 Cloud Cover Assessment (CCA) dataset and then tests the detectability on both Landsat-8 Spatial Procedures for Automated Removal of Cloud and Shadow (SPARCS) and Sentinel-2 dataset.
Based on our results, the current workflow is stable for Landsat-8 and transferable to Sentinel-2 data products. The overall accuracy for Landsat-8 is 95.7% with only 16% of commission error. After a calibration from short-wave infrared (SWIR), the accuracy could reach up to 97.4% with only 7.5% commission error. For Sentinel -2, the omission error for cloud class, exclude thin cirrus, could lower to 3% with the commission error below 1% on land and water area.
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