| Summary: | Cloud, one of the poor atmospheric conditions, significantly reduces the usability of optical remote-sensing data and hampers follow-up applications. Thus, the identification of cloud remains a priority for various remote-sensing activities, such as product retrieval, land-use/cover classification, object detection, and especially for change detection. However, the complexity of clouds themselves make it difficult to detect thin clouds and small isolated clouds. To accurately detect clouds in satellite imagery, we propose a novel neural network named the Pyramid Contextual Network (PCNet). Considering the limited applicability of a regular convolution kernel, we employed a Dilated Residual Block (DRB) to extend the receptive field of the network, which contains a dilated convolution and residual connection. To improve the detection ability for thin clouds, the proposed new model, pyramid contextual block (PCB), was used to generate global information at different scales. FengYun-3D MERSI-II remote-sensing images covering China with 14,165 × 24,659 pixels, acquired on 17 July 2019, are processed to conduct cloud-detection experiments. Experimental results show that the overall precision rates of the trained network reach <b><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>97.1</mn><mo>%</mo></mrow></semantics></math></inline-formula></b> and the overall recall rates reach <b><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>93.2</mn><mo>%</mo></mrow></semantics></math></inline-formula></b>, which performs better both in quantity and quality than U-Net, UNet++, UNet3+, PSPNet and DeepLabV3+.
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