Hyperspectral Image Classification using Trilateral Filter and Deep Learning

The high spectral variability and resolution make hyperspectral image classification difficult. Most of the previous researches focused on solving its high dimensionality problem; very few types of researches have focused on using edge information of the spectral bands to carry out classification. This paper aims to compare two edge filtering methods-bilateral filter and trilateral filter to extract edge information of hyperspectral images and then use those features for further classification. Minimum Noise Fraction (MNF) and Principal Component Analysis (PCA) were separately used for dimensionality reduction. The images obtained from both the methods were stacked, and the two edge filters were applied to them individually. The final feature images were classified using Convolutional Neural Network (CNN). The proposed methodology was applied to the Indian Pines dataset. The results obtained are satisfactory and have been compared to state of the art (SOTA) techniques.