7 November 2008 Comparison of land cover classification methods based on single-temporal MODIS data
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Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images; 71470H (2008); doi: 10.1117/12.813218
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
Based on single-temporal MODIS data of Gansu province, mainly using its spectra information, three classifiers - the Maximum likelihood, BP neural network and decision tree based on data mining software See 5.0 are applied in the Land cover classification research. The validated results show that decision tree algorithm has the best performance of extraction with an overall accuracy of 82.13 percent, followed by the BP network algorithm, and that of the maximum likelihood classifier is worst; the accuracy of low vegetation area is improved with the indexes of TVA and TVD; Data mining software of See 5.0 with boosting technique can build decision tree quickly and improve the precision of miscible classes.
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Tao Han, Xiaotao Xu, Yaohui Li, Yaowen Xie, "Comparison of land cover classification methods based on single-temporal MODIS data", Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470H (7 November 2008); doi: 10.1117/12.813218; https://doi.org/10.1117/12.813218
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KEYWORDS
Image classification

Neural networks

MODIS

Vegetation

Data mining

Climatology

Earth observing sensors

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