# Spatial-Spectral Regularized Local Scaling Cut for Dimensionality Reduction in Hyperspectral Image Classification

## TL;DR

This paper introduces a novel graph-based dimensionality reduction method for hyperspectral images that leverages both spectral and spatial information to improve classification accuracy, especially with limited training samples.

## Contribution

The proposed SSRLSC method uniquely combines spectral and spatial domain information using regularized local scaling cut and guided filtering for enhanced hyperspectral image classification.

## Key findings

- Outperforms spectral-only methods in classification accuracy
- Effectively preserves pixel consistency with guided filtering
- Demonstrates robustness on real-world datasets

## Abstract

Dimensionality reduction (DR) methods have attracted extensive attention to provide discriminative information and reduce the computational burden of the hyperspectral image (HSI) classification. However, the DR methods face many challenges due to limited training samples with high dimensional spectra. To address this issue, a graph-based spatial and spectral regularized local scaling cut (SSRLSC) for DR of HSI data is proposed. The underlying idea of the proposed method is to utilize the information from both the spectral and spatial domains to achieve better classification accuracy than its spectral domain counterpart. In SSRLSC, a guided filter is initially used to smoothen and homogenize the pixels of the HSI data in order to preserve the pixel consistency. This is followed by generation of between-class and within-class dissimilarity matrices in both spectral and spatial domains by regularized local scaling cut (RLSC) and neighboring pixel local scaling cut (NPLSC) respectively. Finally, we obtain the projection matrix by optimizing the updated spatial-spectral between-class and total-class dissimilarity. The effectiveness of the proposed DR algorithm is illustrated with two popular real-world HSI datasets.