7 November 2008 Beijing-1 small satellite multi-spectrum image classification based on neighborhood EM algorithm and its uncertainty assessment
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Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images; 71470I (2008) https://doi.org/10.1117/12.813219
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
In order to overcome the deficiencies of traditional uncertainty assessment methods of remote sensing images classification by error-matrix and kappa coefficient, classification uncertainties at pixel scale of Beijing-1 small satellite multi-spectrum remote sensing images were measured and represented. Firstly, an unsupervised classification algorithm-neighborhood EM considering spatial autocorrelation and classification fuzziness-was introduced. Then, four uncertainty assessment indexes of neighborhood EM classification-fuzzy membership residual, relative maximum fuzzy membership deviation, fuzzy membership entropy and relative fuzzy membership entropy - were constructed. Finally, the experiments concerned were performed using Beijing-1 small satellite multi-spectrum remote sensing image data in Dongkunlun, Qinghai province, China.
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Binbin He, "Beijing-1 small satellite multi-spectrum image classification based on neighborhood EM algorithm and its uncertainty assessment", Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470I (7 November 2008); doi: 10.1117/12.813219; https://doi.org/10.1117/12.813219
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