Deep support vector machine for hyperspectral image classification
作者: Onuwa OkwuashiChristopher E. Ndehedehe
作者单位: 1Department of Geoinformatics and Surveying, University of Uyo, P.M.B 1017, Uyo, Nigeria
2Australian Rivers Institute and Griffith School of Environment & Science, Griffith University, Nathan, Queensland 4111, Australia
刊名: Pattern Recognition, 2020, Vol.103
来源数据库: Elsevier Journal
DOI: 10.1016/j.patcog.2020.107298
关键词: Remote sensingHyperspectral imageDeep support vector machineImage classification
原始语种摘要: Abstract(#br)To improve on the robustness of traditional machine learning approaches, emphasis has recently shifted to the integration of such methods with Deep Learning techniques. However, the classification problems, complexity and inconsistency in several spectral classifiers developed for hyperspectral images are some reasons warranting further research. This study investigates the application of Deep Support Vector Machine (DSVM) for hyperspectral image classification. Two hyperspectral images, Indian Pines and University of Pavia are used as tentative test beds for the experiment. The DSVM is implemented with four kernel functions: Exponential Radial Basis Function (ERBF), Gaussian Radial Basis Function (GRBF), neural and polynomial. Stand-alone Support Vector Machines form the...
全文获取路径: Elsevier  (合作)
影响因子:2.632 (2012)

  • machine 机器
  • image 
  • vector 矢量
  • classification 分类
  • kernel 
  • network 网络
  • Vector 矢量
  • integration 集成
  • robustness 坚固性
  • SVM 共享虚拟存储器