Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images
This work addresses hyperspectral image classification, an incremental improvement by applying L1-norm to an existing graph-based method for better noise robustness.
The paper tackled the problem of dimensionality reduction for hyperspectral image classification by proposing L1-Scaling Cut (L1-SC), a method that uses L1-norm to generate an optimal projection matrix robust to noise and outliers, achieving effective classification results on both noisy and noiseless data.
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.