Cloud detection of space-borne video remote sensing using improved Unet method

Chongbin Xu; Shengling Geng; Defang Wang; Mingquan Zhou

doi:10.1117/12.2626516

21 December 2021 Cloud detection of space-borne video remote sensing using improved Unet method

Chongbin Xu, Shengling Geng, Defang Wang, Mingquan Zhou

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 1215618 (2021) https://doi.org/10.1117/12.2626516
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

Cloud detection is important for the application of space-borne video remote sensing. Video data of Chinese Jilin-1 is detected through migration learning and improved Unet with fully connected conditional random field. Due to the interference of fast movement of cloud targets and satellite platform jitter in video satellite remote sensing, it is difficult for Unet network depth to effectively extract the context characteristics of cloud targets, and effect of segmentation and cloud detection is poor. To solve the problem of missing cloud target extraction features, this paper uses the VGG16 pretraining model as the backbone network of the context path, and refines the segmentation results using the fully connected conditional random field (Fully Connected / Dense CRF) to improve cloud boundary pixel localization. The results show that the proposed algorithm can effectively improve the model segmentation accuracy, where the accuracy and intersection ratio reach 92.6% and 90.9%. The proposed network has strong generalization and high practical application value.

Citation Download Citation

Chongbin Xu, Shengling Geng, Defang Wang, and Mingquan Zhou "Cloud detection of space-borne video remote sensing using improved Unet method", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 1215618 (21 December 2021); https://doi.org/10.1117/12.2626516

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KEYWORDS

Clouds

Image segmentation

Video

Remote sensing

Feature extraction

RGB color model

Network architectures

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