Anomaly detection in clutter using spectrally enhanced LADAR

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

doi:10.1117/12.2176060

19 May 2015 Anomaly detection in clutter using spectrally enhanced LADAR

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

Proceedings Volume 9465, Laser Radar Technology and Applications XX; and Atmospheric Propagation XII; 946508 (2015) https://doi.org/10.1117/12.2176060
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Puneet S. Chhabra, Andrew M. Wallace, and James R. Hopgood "Anomaly detection in clutter using spectrally enhanced LADAR", Proc. SPIE 9465, Laser Radar Technology and Applications XX; and Atmospheric Propagation XII, 946508 (19 May 2015); https://doi.org/10.1117/12.2176060

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