Next Article in Journal / Special Issue
Semi-Supervised Ground-to-Aerial Adaptation with Heterogeneous Features Learning for Scene Classification
Previous Article in Journal
A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
Open AccessArticle

Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks

1
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
2
Chinese Academy of Surveying and Mapping, Beijing 100830, China
3
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
4
ICube Laboratory, University of Strasbourg, 67000 Strasbourg, France
5
Mining Engineering Institute, Heilongjiang University of Science and Technology, Harbin 150027, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2018, 7(5), 181; https://doi.org/10.3390/ijgi7050181
Received: 5 April 2018 / Revised: 5 May 2018 / Accepted: 7 May 2018 / Published: 9 May 2018
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. View Full-Text
Keywords: multiple convolutional neural networks; cloud detection; superpixel; high-resolution remote sensing imagery multiple convolutional neural networks; cloud detection; superpixel; high-resolution remote sensing imagery
Show Figures

Figure 1

MDPI and ACS Style

Chen, Y.; Fan, R.; Bilal, M.; Yang, X.; Wang, J.; Li, W. Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks. ISPRS Int. J. Geo-Inf. 2018, 7, 181. https://doi.org/10.3390/ijgi7050181

AMA Style

Chen Y, Fan R, Bilal M, Yang X, Wang J, Li W. Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks. ISPRS International Journal of Geo-Information. 2018; 7(5):181. https://doi.org/10.3390/ijgi7050181

Chicago/Turabian Style

Chen, Yang; Fan, Rongshuang; Bilal, Muhammad; Yang, Xiucheng; Wang, Jingxue; Li, Wei. 2018. "Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks" ISPRS Int. J. Geo-Inf. 7, no. 5: 181. https://doi.org/10.3390/ijgi7050181

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop