Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images
Abstract
In this paper, we propose an L1 normalized graph based dimensionality reduction method for Hyperspectral images, called as L1-Scaling Cut (L1-SC). The underlying idea of this method is to generate the optimal projection matrix by retaining the original distribution of the data. Though L2-norm is generally preferred for computation, it is sensitive to noise and outliers. However, L1-norm is robust to them. Therefore, we obtain the optimal projection matrix by maximizing the ratio of between-class dispersion to within-class dispersion using L1-norm. Furthermore, an iterative algorithm is described to solve the optimization problem. The experimental results of the HSI classification confirm the effectiveness of the proposed L1-SC method on both noisy and noiseless data.
Keywords
Cite
@article{arxiv.1709.02920,
title = {Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images},
author = {Ramanarayan Mohanty and S L Happy and Aurobinda Routray},
journal= {arXiv preprint arXiv:1709.02920},
year = {2017}
}
Comments
European Signal Processing Conference 2017