A Multi-scale features-based cloud detection method for Suomi-NPP VIIRS day and night imagery

Abstract

Abstract. Cloud detection is a necessary step before the application of remote sensing images. However, most methods focus on cloud detection in daytime remote sensing images. The ignored nighttime remote sensing images play more and more important role in many fields such as urban monitoring, population estimation and disaster assessment. The radiation intensity similarity between artificial lights and clouds is higher in nighttime remote sensing images than in daytime remote sensing images, which makes it difficult to distinguish artificial lights from clouds. Therefore, this paper proposes a deep learning-based method (MFFCD-Net) to detect clouds for day and nighttime remote sensing images. MFFCD-Net is designed based on the encoder-decoder structure. The encoder adopts Resnet-50 as the backbone network for better feature extraction, and a dilated residual up-sampling module (DR-UP) is designed in the decoder for up-sampling feature maps while enlarging the receptive field. A multi-scale feature extraction fusion module (MFEF) is designed to enhance the ability of the MFFCD-Net to distinguish regular textures of artificial lights and random textures of clouds. An Global Feature Recovery Fusion Module (GFRF Module) is designed to select and fuse the feature in the encoding stage and the feature in the decoding stage, thus to achieve better cloud detection accuracy. This is the first time that a deep learning-based method is designed for cloud detection both in day and nighttime remote sensing images. The experimental results on Suomi-NPP VIIRS DNB images show that MFFCD-Net achieves higher accuracy than baseline methods both in day and nighttime remote sensing images. Results on daytime remote sensing images indicate that MFFCD-Net can obtain better balance on commission and omission rates than baseline methods (92.3% versus 90.5% on F1-score). Although artificial lights introduced strong interference in cloud detection in nighttime remote sensing images, the accuracy values of MFFCD-Net on OA, Precision, Recall, and F1-score are still higher than 90%. This demonstrates that MFFCD-Net can better distinguish artificial lights from clouds than baseline methods in nighttime remote sensing images. The effectiveness of MFFCD-Net proves that it is very promising for cloud detection both in day and nighttime remote sensing images.