Cloud/snow recognition technology has application significance in meteorological detection, aviation control, remote sensing disaster prevention, and mitigation. At present, the method of labeling cloud and snow area manually is time- and labor-consuming. Shallow learning methods not only have limited ability to extract cloud/snow semantic features but also have wake learning and expressive ability. These problems lead to the low accuracy of cloud/snow recognition in shallow learning methods. Deep learning methods extract cloud and snow semantic features layer-by-layer, and this feature extraction improves the accuracy of cloud/snow recognition. However, due to the complexity of cloud/snow texture features and the high similarity of cloud/snow spectral features, it is difficult to obtain satisfactory results for the classification of cloud and snow in plateau areas, by the existing deep learning models. In order to solve the above problems, a multiscale fusion attention network is proposed to recognize cloud and snow areas in plateau remote sensing images. In the proposed model, the main network is DenseNet25, which enhances the propagation and reuse of cloud/snow features in the network. A multiscale fusion is proposed to extract more complex cloud/snow texture and spectral features from spatial dimension. The high weight attention mechanism is introduced to obtain dynamic features based on input, and it is able to improve the discriminating ability of cloud and snow features. The experimental results demonstrate that the proposed model can extract and utilize cloud/snow feature information better than existing models and improve the accuracy and generalization of cloud/snow recognition.
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