چکیده مقاله
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
کلیدواژهها
نویسندگان
Mehrnoosh Omati
Faculty of Geodesy and Geomatics Engineering, K N Toosi University of Technology
Mahmod Reza Sahebi
Faculty of Geodesy and Geomatics Engineering, K N Toosi University of Technology
شیوه ارجاع
Omati, Mehrnoosh and Sahebi, Mahmod Reza,1395,Combination of contextual information and optimal texture features for improving the accuracy of SAR image classification,The 2nd National Conference on Geospatial Information Technology,Tehran
ارائهشده در
مجموعه مقالات دومین کنفرانس ملی مهندسی فناوری اطلاعات مکانی29 دی 1395 · تهران