CLOUD DETECTION BY FUSING MULTI-SCALE CONVOLUTIONAL FEATURES
- 1School of Resource and Environmental Sciences, Wuhan University, Wuhan, P. R. China
- 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, P. R. China
- 3School of Urban Design of Wuhan University, Wuhan University, Wuhan, P. R. China
- 4School of Geodesy and Geomatics, Wuhan University, Wuhan, P. R. China
- 5Collaborative Innovation Center of Geospatial Technology, Wuhan, P. R. China
Keywords: Cloud detection, Deep learning, Convolutional feature fusion, Multi-scale, MSCN
Abstract. Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.