Application of improved D-LinkNet model in cloud detection of domestic satellite image

doi:10.13474/j.cnki.11-2246.2021.338

Abstract

Abstract: Due to the lack of band of most domestic satellites, cloud detection is difficult using traditional methods. In this paper, a cloud detection algorithm based on improved D-LinkNet model is proposed and applied to domestic satellite remote sensing image cloud detection. Firstly, the binary image label is generated by using ResNeSt50-Block with channel attention mechanism. Secondly, the encoder of D-LinkNet50 is improved by using ResNeSt50-Block with channel attention mechanism to replace the original ResNet50-Block. And then the loss function is weighted, and it is found that the cross-entropy loss is the only loss function with higher precision. Finally, the conditional random field (CRF) is used to post-process the predicted results. The experimental results show that the MIoU and precision of the improved D-LinkNet model are improved by 1.93% and 2.45% respectively, and the cloud edge information is kept well. It can be used in cloud detection, and the effect is obviously higher than that of the original D-LinkNet model.

Key words: cloud detection, D-LinkNet, attention mechanism, domestic satellite, conditional random field

CLC Number:

P237

LIU Guangjin, WANG Guanghui, BI Weihua, LIU Huijie, YANG Huachao. Application of improved D-LinkNet model in cloud detection of domestic satellite image[J]. Bulletin of Surveying and Mapping, 2021, 0(11): 54-58,64.

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