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A spectral–textural kernel-based classification method of remotely sensed images

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Abstract

Most studies have been based on the original computation mode of semivariogram and discrete semivariance values. In this paper, a set of texture features are described to improve the accuracy of object-oriented classification in remotely sensed images. So, we proposed a classification method support vector machine (SVM) with spectral information and texture features (ST-SVM), which incorporates texture features in remotely sensed images into SVM. Using kernel methods, the spectral information and texture features are jointly used for the classification by a SVM formulation. Then, the texture features were calculated based on segmented block matrix image objects using the panchromatic band. A comparison of classification results on real-world data sets demonstrates that the texture features in this paper are useful supplement information for the spectral object-oriented classification, and proposed ST-SVM classification accuracy than the traditional SVM method with only spectral information.

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Acknowledgments

The authors are very grateful to the editor and anonymous referees reviews for their valuable comments and helpful suggestions. In addition, this work is supported by National Natural Science Foundation of China (Grant No. 61271386), and the Graduates’ Research Innovation Program of Higher Education of Jiangsu Province of China (Grant No. CXZZ13-0239).

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Authors and Affiliations

  1. College of Computer and Information Engineering, Hohai University, Nanjing, 210098, China

    Jianqiang Gao, Lizhong Xu & Fengchen Huang

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  1. Jianqiang Gao
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  2. Lizhong Xu
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  3. Fengchen Huang
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Correspondence to Jianqiang Gao.

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Gao, J., Xu, L. & Huang, F. A spectral–textural kernel-based classification method of remotely sensed images. Neural Comput & Applic 27, 431–446 (2016). https://doi.org/10.1007/s00521-015-1862-7

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