By increasing spatial resolution of remote sensing images, it is possible to apply spatial information in the classification. This leads to improve the accuracy of multi-spectral images classification. One of the methods for incorporation spatial information is un-supervised segmentation which can be implemented through Expectation Maximization(EM) clustering and connected-component labeling. But this clustering method which always is trapped in local optimum. Therefore, a new algorithm is proposed that can solve the mentioned problem and has better performance in multi-spectral images classification. In order to form a spatial-spectral classifier, first a pixel-wised classifier is applied. Then, after using the LPP approach for dimension reduction, the results of pixel -wised classifier and segmentation map, obtained from proposed method, are combined via majority voting. The results of simulation indicate that the presented spectral-spatial classifier leads to so considerable improvement that the accuracy and validity of classification have reached to 88.68% and 80.8%, respectively.
قاسمیان یزدی, . ., & پورآهنگریان, . (2015). Spectral-Spatial Classification of Multi-spectral Images Based on Improved EM Clustering Technique. Electronics Industries, 6(3), 45-54.
MLA
محمد حسن قاسمیان یزدی; فرشته پورآهنگریان. "Spectral-Spatial Classification of Multi-spectral Images Based on Improved EM Clustering Technique". Electronics Industries, 6, 3, 2015, 45-54.
HARVARD
قاسمیان یزدی, . ., پورآهنگریان, . (2015). 'Spectral-Spatial Classification of Multi-spectral Images Based on Improved EM Clustering Technique', Electronics Industries, 6(3), pp. 45-54.
VANCOUVER
قاسمیان یزدی, . ., پورآهنگریان, . Spectral-Spatial Classification of Multi-spectral Images Based on Improved EM Clustering Technique. Electronics Industries, 2015; 6(3): 45-54.
)