Cloud detection sample generation algorithm for nighttime satellite imagery based on daytime data and machine learning application.

ABSTRACT: Highly accurate nighttime cloud detection in satellite imagery is challenging due to the absence of visible to near-infrared (0.38-3 μm, VNI) data, which is critical for distinguishing clouds from other ground features. Fortunately, Machine learning (ML) techniques can more effectively leverage the limited wavelength information and show high-accuracy cloud detection based on vast sample volume. However, accurately distinguishing cloud pixels solely through thermal infrared bands (8-14 μm, TIR) is challenging, and acquiring numerous, high-quality and representative samples of nighttime images for ML proves to be unattainable in practice. Given the thermal infrared radiation transmission process and the fact that daytime and nighttime have the same source of radiance in TIR, we propose a sa

SUBMITTER: Shi X 

PROVIDER: S-EPMC11561103 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature