Research Papers

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

[+] Author Affiliations
Weiwei Sun

Ningbo University, College of Architectural Engineering, Civil Engineering and Environment, 818 Fenghua Road, Ningbo, Zhejiang 315211, China

Chun Liu

Tongji University, College of Surveying and Geo-informatics, 1239 Siping Road, Shanghai 200092, China

Key Laboratory of Advanced Engineering Survey of NASMG, 1239 Siping Road, Shanghai 200092, China

Jialin Li

Ningbo University, College of Architectural Engineering, Civil Engineering and Environment, 818 Fenghua Road, Ningbo, Zhejiang 315211, China

Yenming Mark Lai

University of Maryland College Park, Applied Mathematics & Statistics, and Scientific Computation, Maryland 20742

Weiyue Li

Tongji University, College of Surveying and Geo-informatics, 1239 Siping Road, Shanghai 200092, China

J. Appl. Remote Sens. 8(1), 083641 (May 01, 2014). doi:10.1117/1.JRS.8.083641
History: Received November 21, 2013; Revised March 28, 2014; Accepted April 1, 2014
Text Size: A A A

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.

Figures in this Article
© 2014 Society of Photo-Optical Instrumentation Engineers

Topics

Matrices ; Sensors

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", J. Appl. Remote Sens. 8(1), 083641 (May 01, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083641


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Gas Phase Sensing of Alcohols by Metal Organic Framework-Polymer Composite Materials. ACS Appl Mater Interfaces Published online Apr 25, 2017;
Sample treatment procedures for environmental sensing and biosensing. Curr Opin Biotechnol Published online Apr 26, 2017;
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.