The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLII-4/W18
https://doi.org/10.5194/isprs-archives-XLII-4-W18-285-2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-285-2019
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

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.