Cloud detection algorithm of remote sensing image based on DenseNet and attention mechanism
LIU Guangjin1,2(), WANG Guanghui1,2(), BI Weihua3, LIU Huijie2, YANG Huachao1
1. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China 2. Land Satellite Remote Sensing Application Center, MNR, Beijing 100048, China 3. Wanbei Coal and Electricity Co.Ltd., Suzhou 234002, China
The cloud detection of remote sensing images is the first step in the process of remote sensing image processing. To address the problem that the traditional cloud detection algorithm has a poor effect on the detection of small and thin clouds, this paper proposes a cloud detection method for densely connected network remote sensing images based on the attention mechanism. First, cloud vectors are manually checked from the images provided by the Land Satellite Remote Sensing Application Center of the Ministry of Natural Resources and cloud labels are made, and the images are preprocessed by sequential clipping, color jitter, rotation, etc. to enlarge the sample size. Then, the pre-processed remote sensing images and their labels are fed into a neural network with DenseNet as the encoder and decoder, and a cascaded atrous convolution module is added between the encoder and decoder to increase the receptive field, and a dual attention mechanism and a global context modeling module are added to suppress some irrelevant detailed information. Finally, the experimental results showed that the accuracy rate could reach 95% and the intersection over union could reach 91%, which are big improvements over the traditional cloud detection algorithm, and this method performs well in extracting small and thin clouds.
刘广进, 王光辉, 毕卫华, 刘慧杰, 杨化超. 基于DenseNet与注意力机制的遥感影像云检测算法[J]. 自然资源遥感, 2022, 34(2): 88-96.
LIU Guangjin, WANG Guanghui, BI Weihua, LIU Huijie, YANG Huachao. Cloud detection algorithm of remote sensing image based on DenseNet and attention mechanism. Remote Sensing for Natural Resources, 2022, 34(2): 88-96.
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