期刊
TENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2018)
卷 10806, 期 -, 页码 -出版社
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2503034
关键词
cloud detection; convolution neural network; deep learning; Landsat-8
资金
- National Natural Science Foundation of China [61401474]
- Hunan Provincial Natural Science Foundation [2016JJ3025]
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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