Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval

TL;DR

This paper introduces a graph-regularized low rank representation model that enhances aerosol optical depth retrieval by capturing local data structures and nonlinearities, leading to improved accuracy over existing models.

Contribution

It presents a novel combination of low rank representation and graph regularization for AOD retrieval, integrating satellite data as a graph to improve spectral feature learning.

Findings

Outperforms physical and empirical models in accuracy

Effective in capturing local structure and nonlinearities

Validated on multiple satellite datasets

Abstract

In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval. First, we adopt a low rank representation (LRR) model to learn a powerful representation of the spectral response. Then, graph regularization is incorporated into the LRR model to capture the local structure information and the nonlinear property of the remote-sensing data. Since it is easy to acquire the rich satellite-retrieval results, we use them as a baseline to construct the graph. Finally, the learned feature representation is feeded into support vector machine (SVM) to retrieve AOD. Experiments are conducted on two widely used data sets acquired by different sensors, and the experimental results show that the proposed method can achieve superior performance compared to the physical models and other state-of-the-art empirical models.