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

作者：Gao, Jianqiang^*; Xu, Lizhong; Huang, Fengchen

来源：Neural Computing & Applications, 2016, 27(2): 431-446.

DOI：10.1007/s00521-015-1862-7

• SVM
• ST-SVM
• Kernel method
• Remotely sensed images classification

Most studies have been based on the original computation mode of semivariogram and discrete semi-variance 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.

• 出版日期2016-2
• 单位河海大学