Anomaly detection uses spectral pixels to distinguish between one pixel or group of pixels in a hyperspectral image and itstheir background pixels. Most of the anomaly detection algorithms depend on the assumptions of the background distribution such as the RX algorithm which assumes the gaussian distribution of the background which is not valid for most cases of hyperspectral images. Moreover, most of the algorithms have problems with the false alarms which is noise and detected as anomalies. To overcome these drawbacks, we propose a simple and easy anomaly detection algorithm which depends mainly on the spectral unmixing. Instead of using the raw pixels as given data to detect anomalies, we apply the spectral unmixing algorithm first to estimate the abundance maps and use these maps as features for anomaly detection. Next, we use edge detection algorithm for all abundance maps to detect all boundaries and anomalies in the scene. This gives robustness to the detection algorithm as every anomaly is detected in two abundance maps. We used AVIRIS hyperspectral imaging data cubes to evaluate the proposed algorithm.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.