Passive infrared (IR) remote sensors are gaining wide acceptance as an analytical tool for the remote detection of chemical vapor plumes. A common problem in plume detection for remotely sensed data is the ability to obtain a quality background signature. Many detection methods employ techniques to extract the signatures of the unknown components in order to determine the overall classification of a desired signature. However, this is often the most difficult step since no prior background knowledge is available. In this document, a novel implementation of partial least squares (PLS) regression is proposed for the automatic detection of dimethylmethylphosphonate (DMMP) vapors from remotely sensed hyperspectral image data. In this implementation, prior knowledge of the target signature is used to extract the analyte information directly from the scene. The various unknown and interfering signatures are implicitly modeled by the PLS algorithm as components that maximize a covariance criterion. This implicit modeling is beneficial since it allows for the detection of a single target chemical without the need for a separate background subtraction procedure.
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.