# Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

## TL;DR

This paper introduces a novel regularized low rank representation model that incorporates local geometric structure and spatial-spectral features to improve hyperspectral image classification accuracy.

## Contribution

The paper proposes the LSLRR model with locality and structure regularization, enhancing classical LRR for better segmentation and discrimination in hyperspectral data.

## Key findings

- LSLRR outperforms state-of-the-art methods on three HSI datasets.
- The model effectively exploits local and global data structures.
- Incorporating spatial-spectral features improves classification accuracy.

## Abstract

Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying lowdimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality and structure regularized low rank representation (LSLRR) model is proposed for HSI classification. To overcome the above limitations, we present locality constraint criterion (LCC) and structure preserving strategy (SPS) to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. Besides, we propose a structure constraint to make the representation have a near block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI datasets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.