شماره ركورد كنفرانس :
2527
عنوان مقاله :
Combination of contextual information and optimal texture features for improving the accuracy of SAR image classification
پديدآورندگان :
Omati Mehrnoosh نويسنده Faculty of Geodesy and Geomatics Engineering, K.N Toosi University of Technology , Sahebi Mahmod Reza نويسنده Faculty of Geodesy and Geomatics Engineering, K.N Toosi University of Technology
كليدواژه :
Synthetic aperture radar (SAR) , Markov random field (MRF) , Texture features , Support vector machine (SVM)
عنوان كنفرانس :
دومين كنفرانس ملي مهندسي فناوري اطلاعات مكاني
چكيده لاتين :
This paper employs the full advantages of contextual information and optimal texture features for improving the accuracy of pixel-based classification. In the proposed novel classification method, first, optimal texture features are selected based on the genetic algorithm (GA) and Jeffries-Matusita (JM) distance criterion. Second, the selected texture features are combined with backscattering SAR data, and a support vector machine (SVM) pixel-based classification is done. Finally, integration of Gaussian Markov random field (MRF) model with SVM classifier obtains final classification map. Comparison of the proposed method with pixel-based classification shows a 13.77% improvement in overall classification accuracy of TerraSAR-X images
شماره مدرك كنفرانس :
4411740
