A spectral–textural kernel-based classification method of remotely sensed images. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Gao, Jianqiang; Xu, Lizhong; Huang, Fengchen
• Is Part Of: Neural computing & applications. Volume 27:Issue 2(2016)
• Page Start: 431
• Page End: 446
• Publication Date: 2016-02
• Subjects: SVM -- ST-SVM -- Kernel method -- Remotely sensed images classification; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1862-7
• Languages: English
• ISSNs: 0941-0643

Abstract

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.