Graph-regularized low-rank representation for aerosol optical depth retrieval. Issue 24 (16th December 2016)

• Record Type: Journal Article
• Authors: Sun, Yubao; Hang, Renlong; Liu, Qingshan; Zhu, Fuping; Pei, Hucheng
• Is Part Of: International journal of remote sensing. Volume 37:Issue 24(2016)
• Page Start: 5749
• Page End: 5762
• Publication Date: 2016-12-16
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1249302
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: In this article, 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, a graph regularization is incorporated into the LRR model to capture the local structure information and the non-linear property of the remote-sensing data. Since it is easy to acquire the rich results retrieved by satellite, we use them as a baseline to construct the graph. Finally, the learned feature representation is fed into Support Vector Machines (SVMs) 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.