Image construction using multitemporal observations and Dynamic Detection Models
This paper systematically derives and analyses the generic phenomenon of space-invariant predictability of spatio-temporal observation fields using past multitemporal observations. We focus on thermal infrared remote sensing as a non-trivial example illustrating the predictability concept. The phenomenon and the systematic analysis thereof are experimentally demonstrated to be productive for developing effective automated anomaly detection and classification methods operating under the assumption of dynamic environment and sensor response. Using a simple preliminary experiment involving uncalibrated tower-based high-resolution thermal infrared surveillance, we test the conceptual validity of the space-invariant multitemporal prediction and exemplify its potential applications. In addition, we use a MODIS thermal image sequence and the task of hot anomaly detection to demonstrate the applicability of the approach for monitoring the status of large territories from space-borne platforms.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Center for Spatial Technologies and Remote Sensing (CSTARS), University of California Davis, Davis, CA 95616, USA,Department of Geography and the Human Environment, Tel Aviv University, Ramat-Aviv, Tel-Aviv, Israel
Department of Geography and the Human Environment, Tel Aviv University, Ramat-Aviv, Tel-Aviv, Israel
Center for Spatial Technologies and Remote Sensing (CSTARS), University of California Davis, Davis, CA 95616, USA
Publication date: January 1, 2009
More about this publication?