Cloud detection for remote sensing images using improved U-Net

doi:10.13474/j.cnki.11-2246.2020.0070

Abstract

Abstract: An improved U-Net model is proposed to solve the problem that missing detection of fragmentary clouds and thin clouds when U-Net is applied to detect clouds, and applied to cloud detection of FY-4A data. Firstly, the cloud inspection product of the National Meteorological Satellite Center is used to generate binary cloud label. Secondly, the encoder of U-Net is combined with residual block to share parameters and avoid degradation of deep network. Finally, the dense block is integrated into the decoder to connect the shallow features with the deep features, which is conducive to acquiring new features and improving the utilization rate of features. The experimental results show that the IOU and Dice coefficients of the model on the test set are 91.5% and 95.2% respectively, which can detect thin clouds and a large number of broken clouds well, and the effect is obviously better than that of the U-Net model.

Key words: cloud detection, U-Net, residual block, dense block, FY-4A

CLC Number:

P237

ZHANG Yonghong, CAI Pengyan, TAO Runzhe, WANG Jiangeng, TIAN Wei. Cloud detection for remote sensing images using improved U-Net[J]. Bulletin of Surveying and Mapping, 2020, 0(3): 17-20,34.

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