Anomaly detection in clutter using spectrally enhanced LADAR

Puneet S. Chhabra, Andrew M. Wallace, James R. Hopgood

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a series of echoes that reflect from objects in a scene. These can be first, last or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure the intensity of light reflected from objects continuously over a period of time. In a camflouaged scenario, e.g., objects hidden behind dense foliage, a FW-Ladar penetrates such foliage and returns a sequence of echoes including buried faint echoes. The aim of this paper is to learn local-patterns of co-occurring echoes characterised by their measured spectra. A deviation from such patterns defines an abnormal event in a forest/tree depth profile. As far as the authors know, neither DR or FW-Ladar, along with several spectral measurements, has not been applied to anomaly detection. This work presents an algorithm that allows detection of spectral and temporal anomalies in FW-Multi Spectral Ladar (FW-MSL) data samples. An anomaly is defined as a full waveform temporal and spectral signature that does not conform to a prior expectation, represented using a learnt subspace (dictionary) and set of coefficients that capture co-occurring local-patterns using an overlapping temporal window. A modified optimization scheme is proposed for subspace learning based on stochastic approximations. The objective function is augmented with a discriminative term that represents the subspace's separability properties and supports anomaly characterisation. The algorithm detects several man-made objects and anomalous spectra hidden in a dense clutter of vegetation and also allows tree species classification.

Original languageEnglish
Title of host publicationLaser Radar Technology and Applications XX; and Atmospheric Propagation XII
PublisherSPIE
Volume9465
DOIs
Publication statusPublished - 2015

Publication series

NameProceedings of SPIE
Volume9465
ISSN (Print)0277-786X

Keywords

  • anomaly detection
  • ATR
  • clutter modelling
  • dictionary learning
  • feature extraction
  • full-waveform
  • Ladar
  • LiDAR
  • multi-spectral
  • sparse representation
  • subspace learning

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Anomaly detection in clutter using spectrally enhanced LADAR'. Together they form a unique fingerprint.

  • Cite this

    Chhabra, P. S., Wallace, A. M., & Hopgood, J. R. (2015). Anomaly detection in clutter using spectrally enhanced LADAR. In Laser Radar Technology and Applications XX; and Atmospheric Propagation XII (Vol. 9465). [946508] (Proceedings of SPIE; Vol. 9465). SPIE. https://doi.org/10.1117/12.2176060