When imaging data is collected using airborne remote sensing systems, it is common that the image quality (IQ) of the collected data is not uniform over the entire region of collection. This non-uniformity of IQ is often a limiting factor to the utility of collected data. It would therefore be useful to have a mechanism to predict, assess and manage the non-uniformity of the IQ of remote sensing data both before and after data collection. A mechanism is proposed to model spatially and temporally varying IQ aspects of an imaging collection as a matrix across the region of collection. Within this framework an image quality metric such as a NIIRS based IQE or other IQ predictor is applied to the matrix of parameters, thus sampling IQ such that a 'map' or 'picture' of image quality is created. This allows specific knowledge of IQ performance at particular locations in an image, allowing better resource management when multiple targets with separate collection requirements are collected in the same imaging event. Application to mission planning and optimization of system resources under contingency operations, such as when a system must operate in a degraded state, are also discussed.
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.