Combining CFAR with anomaly detection at hyperspectral images

Research output: Contribution to journalConference articlepeer-review


Over the last few years, we have developed an algorithm which detects anomalous targets in hyperspectral images. The algorithm takes a hyperspectral cube with a completely unknown background, segments the cube, assigns the largest clusters as background, and determines which pixels are anomalous to the background. In the work to be presented here, we will add two additional modules. First, since our present mission is to detect military targets in a fairly barren rural background, we use the SAVI (or NDVI) metric to detect items which appear to contain chlorophyll. In this way, we can eliminate objects which in retrospect were the right sizes and shapes but were in reality plants. Second, we have developed CFAR methods to achieve a Constant False Alarm Rate while giving us the maximum probability of detecting the targets. Actual data will be analyzed by the algorithm; the ability to both determine if a target is present and where its location is will be shown.

Original languageEnglish
Article number604304
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume6043 I
StatePublished - 1 Dec 2005
EventMIPPR 2005: SAR and Multispectral Image Processing - Wuhan, China
Duration: 31 Oct 20052 Nov 2005


  • Anomaly Detection
  • CFAR
  • Hyperspectral Data
  • Multispectral Data
  • NDVI
  • SAVI
  • SRC

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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