Journal
WATER
Volume 10, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/w10111666
Keywords
cloud and snow detection; convolutional neural networks; superpixel segmentation; multispectral imagery
Categories
Funding
- China Postdoctoral Science Foundation [2017M621229]
- Postdoctoral Science Foundation of Heilongjiang Province [LBH-Z17001]
- China Scholarship Council [201708230012]
- National Key Research and Development Plan of China [2017YFB0503604, 2016YFE0200400]
- National Natural Science Foundation of China [41671442, 41571430, 41271442, 41101177, 41301081]
- Joint Foundation of Ministry of Education of China [6141A02022341]
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Cloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, e.g., Sentinel-2A 1.55-1.75 mu m band) is widely applied to the detection of snow and clouds. However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. To solve this problem, a novel convolutional neural network (CNN) to classify cloud and snow on an object level is proposed in this paper. Specifically, a novel CNN structure capable of learning cloud and snow multiscale semantic features from high-resolution multispectral imagery is presented. In order to solve the shortcoming of salt-and-pepper in pixel level predictions, we extend a simple linear iterative clustering algorithm for segmenting high-resolution multispectral images and generating superpixels. Results demonstrated that the new proposed method can with better precision separate the cloud and snow in the high-resolution image, and results are more accurate and robust compared to the other methods.
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