DocumentCode :
1886909
Title :
Evaluation and Analysis of Environment Satellite(HJ-1) Data about Land Features Classification
Author :
Wang, Yanxia ; Huang, Wanli ; Liu, Yufeng ; Li, Hu
Author_Institution :
Coll. of Geogr. Sci., Fujian Normal Univ., Fuzhou, China
fYear :
2010
fDate :
25-26 Dec. 2010
Firstpage :
1
Lastpage :
5
Abstract :
To study properties about land features information extraction of HJ-1A CCD1 remote environment monitoring images, this paper selected the east area of Nileke forest farm in western Tianshan mountains of China as the study area, used maximum-likelihood classifier, Mahalanobis distance classifier, minimum distance classifier and K-means classifier to classify, compare and analyze at two different scales with Landsat5 TM images. The experimental results show the following three phenomena: (1) different classification scales have different accuracies. In the first land use classification system, the classification accuracy of HJ-1A CCD1 images are lower than TM images, but higher in the second forest land classification system. (2) Accuracy results of maximum-likelihood classification show that it is the best algorithm to classify land use types. In the first land use classification system, TM total accuracy is up to 85.1% and Kappa coefficient is 0.8. In the second land use classification system, the result is up to 85.4% and kappa coefficient is 0.74. and(3) judgment both from the view of visual interpretation and quantitative accuracy testing, unsupervised classification method with K-means classifier has low qualities in where many land features have characters of scattered distribution and small different spectrum information.
Keywords :
feature extraction; geophysics computing; maximum likelihood estimation; HJ-1A CCD1 remote environment monitoring; K-means classifier; Landsat5 TM image; Mahalanobis distance classifier; Nileke forest farm; environment satellite; kappa coefficient; land feature classification; land features information extraction; maximum-likelihood classifier; minimum distance classifier; unsupervised classification method; Accuracy; Charge coupled devices; Classification algorithms; Feature extraction; Green products; Remote sensing; Satellites;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on
Conference_Location :
Wuhan
ISSN :
2156-7379
Print_ISBN :
978-1-4244-7939-9
Electronic_ISBN :
2156-7379
Type :
conf
DOI :
10.1109/ICIECS.2010.5677740
Filename :
5677740
Link To Document :
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