Next Article in Journal
Application of Multiple Approaches to Investigate the Hydrochemistry Evolution of Groundwater in an Arid Region: Nomhon, Northwestern China
Next Article in Special Issue
Evaluation of Evapotranspiration Estimates in the Yellow River Basin against the Water Balance Method
Previous Article in Journal
Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland
Open AccessArticle

Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers

1
College of Architectural Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China
2
College of Landscape Architecture, Northeast Forestry University, Harbin 150040, China
3
College of Agricultural and Life Sciences, University of Wisconsin, Madison, WI 53706, USA
4
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
5
Chinese Academy of Surveying and Mapping, Beijing 100830, China
6
College of Wildlife Resources, Northeast Forestry University, Harbin 150040, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2018, 10(11), 1666; https://doi.org/10.3390/w10111666
Received: 15 October 2018 / Revised: 9 November 2018 / Accepted: 12 November 2018 / Published: 15 November 2018
(This article belongs to the Special Issue Satellite Remote Sensing and Analyses of Climate Variability)
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 µm 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. View Full-Text
Keywords: cloud and snow detection; convolutional neural networks; superpixel segmentation; multispectral imagery cloud and snow detection; convolutional neural networks; superpixel segmentation; multispectral imagery
Show Figures

Figure 1

MDPI and ACS Style

Wang, L.; Chen, Y.; Tang, L.; Fan, R.; Yao, Y. Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers. Water 2018, 10, 1666.

Show more citation formats Show less citations formats
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
Back to TopTop