YNAO OpenIR  > 射电天文研究组
Cloud Feature Recognition and Area Location for Satellite Images based on Information Entropy
Miao, Sheng1; Dong L(董亮)2; Gao, Hao1; Wang, Xiaorui1
Source PublicationInternational Conference on Intelligent Human-Machine Systems and Cybernetics - 2016 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL. 2
2016
Volume2
Pages300-302
DOI10.1109/IHMSC.2016.250
Author of SourceIEEE
Contribution Rank第2完成单位
Indexed ByEI ; CPCI
Conference Name8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)
Conference Date2016-09-11
Conference PlaceZhejiang Univ, Hangzhou, PEOPLES R CHINA
Conference SponsorIEEE; IEEE Comp Soc; Univ Bristol; Japan Adv Inst Sci & Technol; Beihang Univ
KeywordSatellite Images Cloude Recognition Information Entropy Cloud Feature Area Location
Abstract

Satellite images is the basis of the many subjects research. But usually, many satellite image were covered with cloud more or less. Cloud detection and recognition is very important for satellite analysis. In this paper, we focus on the cloud area recognition and edge detection, our method has two steps, one is cloud image detection, the second step is determine the area of the cloud. The methods include image histogram average and entropy analysis. In the experiment, we contrast cloud detection in different scale image, each one has each shape cloud. The experiment show our method can effectively detect the cloud and the cloud area can also be determined.

Funding ProjectYouth Fund of National Natural Science Foundation of China[11303094] ; Natural Science Foundation Astronomy joint fund of China[U1431113] ; applied basic research program and project of Yunnan province of China[2015FB189] ; Western Light A class talent program of China and Research Center of Kunming Forestry Information Engineering Technology[2015FIB05]
Funding OrganizationYouth Fund of National Natural Science Foundation of China ; Natural Science Foundation Astronomy joint fund of China ; applied basic research program and project of Yunnan province of China ; Western Light A class talent program of China and Research Center of Kunming Forestry Information Engineering Technology
Language英语
Subject Area天文学
MOST Discipline Catalogue理学 ; 理学::天文学
SubtypeProceedings Paper
PublisherIEEE
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
ISBN978-1-5090-0768-4
ISSN2157-8982
URL查看原文
WOS IDWOS:000391330900069
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS KeywordEnhancement
EI Accession Number20170403289156
EI KeywordsSatellites
EI Classification Number655.2satellites
Citation statistics
Document Type会议论文
Identifierhttp://ir.ynao.ac.cn/handle/114a53/9929
Collection射电天文研究组
Corresponding AuthorDong L(董亮)
Affiliation1.Southwest Forestry University School of Computer and Information, Kunming , China
2.Radio astronomy research group in Yunnan observatory of Chinese academy of science, Kunming , China
Recommended Citation
GB/T 7714
Miao, Sheng,Dong L,Gao, Hao,et al. Cloud Feature Recognition and Area Location for Satellite Images based on Information Entropy[C]//IEEE. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2016:300-302.
Files in This Item:
File Name/Size DocType Version Access License
Cloud Feature Recogn(372KB)会议论文 开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Miao, Sheng]'s Articles
[Dong L(董亮)]'s Articles
[Gao, Hao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Miao, Sheng]'s Articles
[Dong L(董亮)]'s Articles
[Gao, Hao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Miao, Sheng]'s Articles
[Dong L(董亮)]'s Articles
[Gao, Hao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Cloud Feature Recognition and Area Location for Satellite Images based on.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.