期刊
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
卷 7, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/ijgi7050181
关键词
multiple convolutional neural networks; cloud detection; superpixel; high-resolution remote sensing imagery
资金
- National Key R&D Program of China [2016YFC0803100]
- Doctoral Program Foundation of Institutions of Higher Education of China [20112121120003]
- National Natural Science Foundation of China [41101452]
In high-resolution image data, multilevel cloud detection is a key task for remote sensing data processing. Generally, it is difficult to obtain high accuracy for multilevel cloud detection when using satellite imagery which only contains visible and near-infrared spectral bands. So, multilevel cloud detection for high-resolution remote sensing imagery is challenging. In this paper, a new multilevel cloud detection technique is proposed based on the multiple convolutional neural networks for high-resolution remote sensing imagery. In order to avoid input the entire image into the network for cloud detection, the adaptive simple linear iterative clustering (A-SCLI) algorithm was applied to the segmentation of the satellite image to obtain good-quality superpixels. After that, a new multiple convolutional neural networks (MCNNs) architecture is designed to extract multiscale features from each superpixel, and the superpixels are marked as thin cloud, thick cloud, cloud shadow, and non-cloud. The results suggest that the proposed method can detect multilevel clouds and obtain a high accuracy for high-resolution remote sensing imagery.
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