Back to Search
Start Over
Multi-temporal remote sensing data applied in automatic land cover update using iterative training sample selection and Markov Random Field model
- Source :
- Geocarto International. 30:882-893
- Publication Year :
- 2015
- Publisher :
- Informa UK Limited, 2015.
-
Abstract
- Automatic land cover update was an effective means to obtain objective and timely land cover maps without human disturbance. This study investigated the efficacy of multi-temporal remote sensing data and advanced non-parametric classifier on improving the classification accuracy of the automatic land cover update approach integrating iterative training sample selection and Markov Random Fields model when the historical remote sensing data were unavailable. The results indicated that two-temporal remote sensing data acquired in one crop growth season could significantly improve the classification accuracy of the automatic land cover update approach by approximately 3–4%. However, the support vector machine (SVM) classifier was not suitable to be integrated in the automatic land cover update approach, because the huge initially selected training samples made the training of the SVM classifier unrealizable.
- Subjects :
- Sample selection
Random field
Markov random field
Markov chain
business.industry
Geography, Planning and Development
Land cover
computer.software_genre
Machine learning
Support vector machine
Geography
Data Applied
Data mining
Artificial intelligence
business
computer
Classifier (UML)
Water Science and Technology
Remote sensing
Subjects
Details
- ISSN :
- 17520762 and 10106049
- Volume :
- 30
- Database :
- OpenAIRE
- Journal :
- Geocarto International
- Accession number :
- edsair.doi...........d82975b665365b35fbcfe01f2d1dab91