9 December 2015 Remote sensing image classification based on support vector machine with the multi-scale segmentation
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Proceedings Volume 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015); 98171C (2015) https://doi.org/10.1117/12.2228099
Event: Seventh International Conference on Graphic and Image Processing, 2015, Singapore, Singapore
Abstract
In this paper, we proposed a new classification method based on support vector machine (SVM) combined with multi-scale segmentation. The proposed method obtains satisfactory segmentation results which are based on both the spectral characteristics and the shape parameters of segments. SVM method is used to label all these regions after multiscale segmentation. It can effectively improve the classification results. Firstly, the homogeneity of the object spectra, texture and shape are calculated from the input image. Secondly, multi-scale segmentation method is applied to the RS image. Combining graph theory based optimization with the multi-scale image segmentations, the resulting segments are merged regarding the heterogeneity criteria. Finally, based on the segmentation result, the model of SVM combined with spectrum texture classification is constructed and applied. The results show that the proposed method can effectively improve the remote sensing image classification accuracy and classification efficiency.
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Wenxing Bao, Wenxing Bao, Wei Feng, Wei Feng, Ruishi Ma, Ruishi Ma, "Remote sensing image classification based on support vector machine with the multi-scale segmentation", Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98171C (9 December 2015); doi: 10.1117/12.2228099; https://doi.org/10.1117/12.2228099
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