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Accurate cloud detection is a prerequisite for remote sensing image applications. Cloud detection over bright surfaces is always a complex problem. Because of the similar spectral characteristics of clouds and bright surfaces, it is challenging to identify clouds from bright surfaces accurately. A novel MBUNet framework is proposed to improve cloud detection in remote sensing images of bright underlying surfaces. The network uses Bi-directional convolutional LSTM (BConvLSTM) to obtain multi-scale feature information by a nonlinear combination of low and high-dimensional image features, extract global features, and reduce information loss. Multi-scale context attention module (MCAM) enables the network to focus on the cloud region, suppress the non-cloud region, and learn delicate high-dimensional semantic information in conjunction with context information, thus improving the expressing ability characteristics and getting accurate cloud mask. In addition, a new loss function is used to focus on foreground information and boundary information. Cloud detection experiments were carried out on Landsat 8 Biome and GF-1WFV datasets, respectively, and the results show that the method performs well in cloud detection over bright surfaces, effectively reducing the misjudgment between clouds and bright surface objects.

Improving Cloud Detection over Bright Underlying Surfaces Using a Novel MBUNet Framework for Remote Sensing Imagery