Intercomparison of Cloud Detection Methods Using VNIR Bands

Accurate cloud detection is a crucial step in analyzing optical satellite imagery.The increasing application of small satellites equipped only with Visible and Near-Infrared (VNIR) sensors necessitates the evaluation of cloud detection under limited spectral bands.This study focuses on the intercomparison of three cloud detection methodologies-a custom threshold-based method, a machine learning model (Extreme Gradient Boosting; XGBoost), and a deep learning model (U-Net)-using only five VNIR bands from the CloudSEN12+ dataset.As a result, the U-Net model demonstrated higher performance than other approaches, achieving an Overall Accuracy (OA) of 85.6% and an Intersection over Union (IoU) of 65.3%.In particular, the U-Net significantly outperformed others in detecting cloud shadows, achieving an accuracy of 77.4%, nearly double that of XGBoost (46.0%).In spatial analysis, the threshold method misclassified bright surfaces as clouds, and XGBoost generated salt-and-pepper noise at the pixel level.The U-Net accurately detected the complex boundaries of clouds and shadows.Our results indicate that deep learning approaches that leverage spatial information are highly effective for cloud detection with limited spectral bands.This study provides a quantitative baseline to inform the development of operational cloud-detection algorithms for VNIR-only satellite missions.