Paper Info
Title
Automatic Recognition of Cloud Images by Using Visual Saliency Features
Abstract
Automatic cloud detection from satellite imagery is a necessary preprocessing step in remote sensing. Given that humans can easily “see” clouds in an image because of salient region features, we adopt a visual attention technique in computer vision to automatically identify images with a significant cloud cover. The proposed method generates a rough cloud mask by using a top-down visual saliency model to qualitatively distinguish cloud images from noncloud images. First, an image is downsized for rapid processing. Some basic saliency maps of clouds are then generated by multilevel segmentation, the computation of cloud visual saliency features, and feature classification. Thereafter, we fuse the basic saliency maps by using a most-votes-win strategy to generate the cloud mask. With the cloud mask, a threshold is used to classify the images as cloud or noncloud images. A total of 200 RapidEye images are tested by using the algorithm. Of the cloud images, 92% are correctly identified. The average processing time is 1.8 s per image.
Year
DOI
Venue
2015
10.1109/LGRS.2015.2424531
Geoscience and Remote Sensing Letters, IEEE
Keywords
Field
DocType
classification,cloud detection,saliency map,visual saliency,visualization,satellites,feature extraction,image segmentation,remote sensing,image recognition
Computer vision,Satellite imagery,Salience (neuroscience),Visualization,Segmentation,Remote sensing,Image segmentation,Feature extraction,Artificial intelligence,Mathematics,Cloud cover,Cloud computing
Journal
Volume
Issue
ISSN
12
8
1545-598X
Citations 
PageRank 
References 
5
0.44
13
Authors
3
Name
Order
Citations
PageRank
Xiangyun Hu1798.87
Wang, Y.219536.84
J. Shan322020.08