Dual-window-based anomaly detection for hyperspectral imagery

Heesung Kwon; Sandor Z. Der; Nasser M. Nasrabadi

doi:10.1117/12.486056

16 September 2003 Dual-window-based anomaly detection for hyperspectral imagery

Heesung Kwon, Sandor Z. Der, Nasser M. Nasrabadi

Proceedings Volume 5094, Automatic Target Recognition XIII; (2003) https://doi.org/10.1117/12.486056
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

We propose adaptive anomaly detectors that find materials whose spectral characteristics are substantially different from those of the neighboring materials.The target spectral vectors are assumed to have different statistical characteristics from the background vectors. In order to detect anomalies we use a dual rectangular window that separates the local area into two regions-- the inner window region (IWR) and outer window region (OWR). The statistical spectral differences between the IWR and OWR is exploited by generating subspace projection vectors onto which the IWR and OWR vectors are projected. Anomalies are detected if the projection separation between the IWR and OWR vectors is greater than a predefined threshold. Four different methods are used to produce the subspace projection vectors. The four proposed anomaly detectors have been applied to HYDICE (HYperspectral Digital Imagery Collection Experiment)images and the detection performance for each method has been evaluated.

Citation Download Citation

Heesung Kwon, Sandor Z. Der, and Nasser M. Nasrabadi "Dual-window-based anomaly detection for hyperspectral imagery", Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); https://doi.org/10.1117/12.486056

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