Table of Contents Author Guidelines Submit a Manuscript
Journal of Spectroscopy
Volume 2015 (2015), Article ID 969185, 8 pages
Research Article

Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
2Sansom Institute for Health Research and School of Pharmacy and Medical Science, University of South Australia, Adelaide, SA 5001, Australia

Received 25 August 2014; Accepted 13 September 2014

Academic Editor: Tifeng Jiao

Copyright © 2015 Chunyang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.