Supervised Hyperspectral Image Classification using SVM and Linear Discriminant Analysis

Hyperspectral images are used to recognize and determine the objects on the earth’s surface. This image contains more number of spectral bands and classifying the image becoming a difficult task. Problems of higher number of spectral dimensions are addressed through feature extraction and reduction. However, accuracy and computational time are the important challenges involved in the classification of hyperspectral images. Hence in this paper, a supervised method has been developed to classify the hyperspectral image using support vector machine (SVM) and linear discriminant analysis (LDA). In this work, spectral features of the images are extracted and reduced using LDA. Spectral features of hyperspectral images are classified using SVM with RBF kernel like buildings, vegetation fields, etc. The simulation results show that the SVM algorithm combined with LDA has good accuracy and less computational time. Furthermore, the accuracy of classification is enhanced by incorporating the spatial features using edge-preserving filters.

@article{M2020,
  title     = {Supervised Hyperspectral Image Classification using SVM and Linear Discriminant Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {10},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Shambulinga M and G. Sadashivappa},
  doi       = {10.14569/IJACSA.2020.0111050},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111050}
}