Cloud detection of remote sensing images on Landsat-8 by  deep learning

Xiaoshuang Zeng; Jungang Yang; Xinpu Deng; Wei An; Jun Li

doi:10.1117/12.2503034

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9 August 2018 Cloud detection of remote sensing images on Landsat-8 by deep learning

Xiaoshuang Zeng, Jungang Yang, Xinpu Deng, Wei An, Jun Li

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Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108064Y (2018) https://doi.org/10.1117/12.2503034
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Cloud is always the weak and even uninformative area inevitably existing in the remote sensing images, and greatly limits the development of remote sensing applications. Accurate and automatic detection of clouds in satellite scenes is a key problem for the application of remote sensing images. Most of the previous methods use the low-level feature of the cloud, which often generate error results especially with thin cloud or in complex scenes. In this paper, we propose a novel cloud detection method based on deep learning framework for remote sensing images. The designed deep Convolution Neural Network (CNN) which can mine the deep features of cloud consists of three convolution layers and three fully-connected layers. Using the designed network model, we can predict the probability of each image that belongs to cloud region, and then generate the cloud probability map of the image. To demonstrate the effectiveness of the method, we test it on Landsat-8 satellite images. The overall accuracy of our proposed method for cloud detection is higher than 95%. Experimental results indicate that both thin and thick cloud can be well detected with higher accuracy and robustness using our method.

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Xiaoshuang Zeng, Jungang Yang, Xinpu Deng, Wei An, and Jun Li "Cloud detection of remote sensing images on Landsat-8 by deep learning", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108064Y (9 August 2018); https://doi.org/10.1117/12.2503034

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