Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning
Joint Authors
Wang, Liping
Wang, Chunyang
Guo, Zengzhang
Wang, Shuangting
Ma, Chao
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-08-03
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Study on land use/cover can reflect changing rules of population, economy, agricultural structure adjustment, policy, and traffic and provide better service for the regional economic development and urban evolution.
The study on fine land use/cover assessment using hyperspectral image classification is a focal growing area in many fields.
Semisupervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively, has been a new research direction.
In this paper, we proposed improving fine land use/cover assessment based on semisupervised hyperspectral classification method.
The test analysis of study area showed that the advantages of semisupervised classification method could improve the high precision overall classification and objective assessment of land use/cover results.
American Psychological Association (APA)
Wang, Chunyang& Guo, Zengzhang& Wang, Shuangting& Wang, Liping& Ma, Chao. 2015. Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning. Journal of Spectroscopy،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1070365
Modern Language Association (MLA)
Wang, Chunyang…[et al.]. Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning. Journal of Spectroscopy No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1070365
American Medical Association (AMA)
Wang, Chunyang& Guo, Zengzhang& Wang, Shuangting& Wang, Liping& Ma, Chao. Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning. Journal of Spectroscopy. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1070365
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1070365