Cloud and Snow Segmentation in Satellite Images Using an Encoder–Decoder Deep Convolutional Neural Networks

The segmentation of cloud and snow in satellite images is a key step for subsequent image analysis, interpretation, and other applications. In this paper, a cloud and snow segmentation method based on a deep convolutional neural network (DCNN) with enhanced encoder–decoder architecture—ED-CNN—is pro...

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Bibliographic Details
Published in:	ISPRS international journal of geo-information Vol. 10; no. 7; p. 462
Main Authors:	Zheng, Kai, Li, Jiansheng, Ding, Lei, Yang, Jianfeng, Zhang, Xucheng, Zhang, Xun
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.07.2021
Subjects:	Accuracy Artificial neural networks Atmospheric aerosols Cameras cloud and snow detection Clouds CNN Coders data collection Datasets Deep learning encoder–decoder Experiments Image analysis Image processing Image segmentation Labels Machine learning Methods Neural networks Principal components analysis satellite image Satellite imagery Satellites semantic segmentation Semantics Snow Spaceborne remote sensing spatial data TH-1
ISSN:	2220-9964, 2220-9964
Online Access:	Get full text
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