Cloud Detection of Multi-Angle Remote Sensing Image
Based on Deep Learning

Abstract

Abstract: Cloud detection is one of the important tasks for remote sensing image processing. At present, the multi-spectral and multi-channel information is often used in cloud detection of remote sensing image, but the research on the influence of multi-angle information on cloud detection is still insufficient. To explore the influence of multi-angle information as cloud feature on the accuracy of cloud classification, a cloud detection method with multi-angles remote sensing based on deep learning is proposed. The proposed method takes SegNet as backbone network, and trains a multi-angle information based cloud detection model by extracting the remote sensing image feature with multi-angle information. Extensive experimental results demonstrate that the Global Accuracy and the mean intersection over union (MeanIoU) of the proposed method are 91.39% and 83.99% respectively. And the method proves the limitations of single angle cloud detection and the effectiveness of multi-angle information on the improvement of the cloud detection accuracy. In addtion, the influence of different angles on the cloud detection in POLDER is also explored.

Key words: cloud detection, remote sensing image, multi-angle, neural network, SegNet

CLC Number:

TP79

Li Jiaxin, Zhao Peng, Fang Wei∗, Song Shangxiang, . Cloud Detection of Multi-Angle Remote Sensing Image Based on Deep Learning[J]. Journal of Atmospheric and Environmental Optics, 2020, 15(5): 380-392.

References J

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Cloud Detection of Multi-Angle Remote Sensing Image Based on Deep Learning

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