QUASI-INVARIANT BEHAVIOR OF THERMOPHYSICAL FEATURES FOR INTERPRETATION OF MULTISENSOR IMAGERY

Citation
G. Hoekstra et N. Nandhakumar, QUASI-INVARIANT BEHAVIOR OF THERMOPHYSICAL FEATURES FOR INTERPRETATION OF MULTISENSOR IMAGERY, Optical engineering, 35(3), 1996, pp. 708-721
Citations number
37
Categorie Soggetti
Optics
Journal title
ISSN journal
00913286
Volume
35
Issue
3
Year of publication
1996
Pages
708 - 721
Database
ISI
SICI code
0091-3286(1996)35:3<708:QBOTFF>2.0.ZU;2-S
Abstract
The fusion of information from different sensors can provide features that cannot be obtained from either of the sensors. In this paper we e xamine a feature that is obtained from the fusion of information sense d from thermal (long wave infrared) and visual imagery. Robust object recognition requires object features that are invariant to scene condi tions and viewpoint. Previous effort in developing such features using explicit constraints of invariance has mostly considered only visual data. The investigation of the variation of quasi-invariant features f or different ranges of viewing/scene parameters has also been limited to geometric features derived from visual imagery. Such investigation has not been performed for features derived from multisensory imagery or from thermal (infrared) imagery. In this paper we conduct a detaile d analysis of the variation of invariant and quasi-invariant features that have been proposed for the analysis of thermal (infrared) imagery , and also for the integrated analysis of thermal and visual imagery. The features are based on thermophysical models of energy exchange bet ween the object and the scene. Examination of feature variation is bas ed on simulating the various scene and object energy components for va rying scene parameter values and predicting the feature value. This ap proach eliminates the expensive and impractical task of collecting rea l imagery under all possible scene conditions. We develop thermophysic al object models based on equivalent thermal circuits. The models use a small number of nodes to predict the surface temperatures (thermal i mage gray levels) and to predict the feature values on the surfaces of complex objects. This approach reduces the computational cost of simu lation and facilitates model construction. The sensitivity of the feat ures to object and scene parameter variations is evaluated. The featur es are shown to be relatively robust within specific ranges of scene c onditions. (C) 1996 Society of Photo-Optical Instrumentation Engineers .