Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 285–291, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-285-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 285–291, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-285-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

LOCAL BINARY GRAPH FEATURE REDUCTION FOR THREE-DIMENSIONAL GABOR FILTER BASED HYPERSPECTRAL IMAGE CLASSIFICATION

M. Darvishnezhad, H. Ghassemian, and M. Imani M. Darvishnezhad et al.
  • Image Processing and Information Analysis Laboratory, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

Keywords: Hyperspectral, Spectral Features, Spatial Features, Feature Fusion, Three-Dimensional Gabor Filters

Abstract. One of the challenges of the hyperspectral image classification is the fusing spectral and spatial features. There are several methods for fusing features in hyperspectral image classification. Three-Dimensional Gabor Filters are the best method to extract spectral and spatial features simultaneously. However, one of the problems with using the 3D Gabor filter is the high number of extracted features. In this paper, to reducing extracted features from 3D-Gabor filters and increasing the classification accuracy in hyperspectral images, a novel method named Local Binary Graph (LBG) is used. The LBG method uses a local graph to solve the optimization problem, which maps each pixel to the reduced dimension image and improves the McNemar test result in comparison with the existing methods. Finally, the result of the proposed method achieved 96.2% and 92.6% overall accuracy for Pavia University and Indian Pines data set, respectively.