Full Content is available to subscribers

Subscribe/Learn More  >
Proceedings Article

Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images

[+] Author Affiliations
N. Acito, M. Diani, G. Corsini

Univ. of Pisa (Italy)

Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820O (October 18, 2005); doi:10.1117/12.627691
Text Size: A A A
From Conference Volume 5982

  • Image and Signal Processing for Remote Sensing XI
  • Lorenzo Bruzzone
  • Bruges, Belgium | September 19, 2005

abstract

Anomaly detectors reveal the presence of objects/materials in a multi/hyperspectral image simply searching for those pixels whose spectrum differs from the background one (anomalies ). This procedure can be applied directly to the radiance at the sensor level and has the great advantage of avoiding the difficult step of atmospheric correction. The most popular anomaly detector is the RX algorithm derived by Yu and Reed. It is based on the assumption that the pixels, in a region around the one under test, follow a single multivariate Gaussian distribution. Unfortunately, such a hypothesis is generally not met in actual scenarios and a large number of false alarms is usually experienced when the RX algorithm is applied in practice. In this paper, a more general approach to anomaly detection is considered based on the assumption that the background contains different terrain types (clusters) each of them Gaussian distributed. In this approach the parameters of each cluster are estimated and used in the detection process. Two detectors are considered: the SEM-RX and the K-means RX. Both the algorithms follow two steps: first, 1) the parameters of the background clusters are estimated, then, 2) a detection rule based on the RX test is applied. The SEM-RX stems from the GMM and employs the SEM algorithm to estimate the clusters' parameters; instead, the K-means RX resorts to the well known K-means algorithm to obtain the background clusters. An automatic procedure is defined, for both the detectors, to select the number of clusters and a novel criterion is proposed to set the test threshold. The performances of the two detectors are also evaluated on an experimental data set and compared to the ones of the RX algorithm. The comparative analysis is carried out in terms of experimental Receiver Operating Characteristics.

© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

N. Acito ; M. Diani and G. Corsini
"Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images", Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820O (October 18, 2005); doi:10.1117/12.627691; http://dx.doi.org/10.1117/12.627691


Access This Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 for non-members).

Figures

Tables

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

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

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 Proceeding
Sign in or Create a personal account to Buy this proceeding ($15 for members, $18 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.