A hybrid generative adversarial network for weakly-supervised cloud detection in multispectral images.

ABSTRACT: Cloud detection is a crucial step in the optical satellite image processing pipeline for Earth observation. Clouds in optical remote sensing images seriously affect the visibility of the background and greatly reduce the usability of images for land applications. Traditional methods based on thresholding, multi-temporal or multi-spectral information are often specific to a particular satellite sensor. Convolutional Neural Networks for cloud detection often require labeled cloud masks for training that are very time-consuming and expensive to obtain. To overcome these challenges, this paper presents a hybrid cloud detection method based on the synergistic combination of generative adversarial networks (GAN) and a physics-based cloud distortion model (CDM). The proposed weakly-supervised GAN

SUBMITTER: Li J 

PROVIDER: S-EPMC9483037 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature