Title
Cloud Detection for High-Resolution Satellite Imagery Using Machine Learning and Multi-Feature Fusion.
Abstract
The accurate location of clouds in images is prerequisite for many high-resolution satellite imagery applications such as atmospheric correction, land cover classifications, and target recognition. Thus, we propose a novel approach for cloud detection using machine learning and multi-feature fusion based on a comparative analysis of typical spectral, textural, and other feature differences between clouds and backgrounds. To validate this method, we tested it on 102 Gao Fen-1(GF-1) and Gao Fen-2(GF-2) satellite images. The overall accuracy of our multi-feature fusion method for cloud detection was more than 91.45%, and the Kappa coefficient for all the tested images was greater than 80%. The producer and user accuracy were also higher at 93.67% and 95.67%, respectively; both of these values were higher than the values for the other tested feature fusion methods. Our results show that this novel multi-feature approach yields better accuracy than other feature fusion methods. In post-processing, we applied an object-oriented method to remove the influence of highly reflective ground objects and further improved the accuracy. Compared to traditional methods, our new method for cloud detection is accurate, exhibits good scalability, and produces consistent results when mapping clouds of different types and sizes over various land surfaces that contain natural vegetation, agriculture land, built-up areas, and water bodies.
Year
DOI
Venue
2016
10.3390/rs8090715
REMOTE SENSING
Keywords
Field
DocType
multi-feature fusion,cloud detection,high-resolution,machine learning
Remote sensing,Fusion,Cohen's kappa,Artificial intelligence,Land cover,Atmospheric correction,Computer vision,Satellite,Vegetation,Satellite imagery,Geology,Machine learning,Scalability
Journal
Volume
Issue
ISSN
8
9
2072-4292
Citations 
PageRank 
References 
2
0.39
0
Authors
5
Name
Order
Citations
PageRank
ting bai1469.91
Deren Li262074.26
Kaimin Sun3215.40
Yepei Chen471.95
Wenzhuo Li5315.70