Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery

Weiwei Sun; Chun Liu; Jialin Li; Yenming Mark Lai; Weiyue Li

doi:10.1117/1.JRS.8.083641

1 May 2014 Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery

Weiwei Sun, Chun Liu, Jialin Li, Yenming Mark Lai, Weiyue Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083641 (May 2014). https://doi.org/10.1117/1.JRS.8.083641

Abstract

A low-rank and sparse matrix decomposition (LRaSMD) detector has been proposed to detect anomalies in hyperspectral imagery (HSI). The detector assumes background images are low-rank while anomalies are gross errors that are sparsely distributed throughout the image scene. By solving a constrained convex optimization problem, the LRaSMD detector separates the anomalies from the background. This protects the background model from corruption. An anomaly value for each pixel is calculated using the Euclidean distance, and anomalies are determined by thresholding the anomaly value. Four groups of experiments on three widely used HSI datasets are designed to completely analyze the performances of the new detector. Experimental results show that the LRaSMD detector outperforms the global Reed-Xiaoli (GRX), the orthogonal subspace projection-GRX, and the cluster-based detectors. Moreover, the results show that LRaSMD achieves equal or better detection performance than the local support vector data description detector within a shorter computational time.

Citation Download Citation

Weiwei Sun, Chun Liu, Jialin Li, Yenming Mark Lai, and Weiyue Li "Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery," Journal of Applied Remote Sensing 8(1), 083641 (1 May 2014). https://doi.org/10.1117/1.JRS.8.083641

Published: 1 May 2014

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available