Enhanced DIRSIG scene simulation by incorporating process models

Abstract

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool is a first principles-based synthetic image generation model, developed at the Rochester Institute of Technology (RIT) over the past 20+ years. By calculating the sensor reaching radiance between the bandpass 0.2 to 20μm, it produces multi or hyperspectral remote sensing images. By integrating independent first principles based sub-models, such as MODTRAN, DIRSIG generates a representation of what a sensor would see with high radiometric fidelity. Currently, DIRSIG only models spatial/spectral synthetic images. In order to detect temporal changes in a process within the scene, a process model, which links the observable signatures of interest temporally, should be developed and incorporated into DIRSIG. These process models could be external time-dependent sub-models or pre-defined process models by the users to predict the state of the objects in the scene at a specific time. In this paper, a notional system of two tanks connected by a pipe is built, with hot water coming into tank A through a second pipe and with cooled water released from tank B through a third pipe. This is a simple hydrodynamic & thermodynamic model, controlled by the state of valves in the scenario. The initial temperature and height of the water in the two tanks are pre-defined by the user. Surface temperatures as a function of time are then predicted and captured as characterization maps, which are then mapped onto DIRSIG geometry using UV mapping technique. Finally, a spatial-spectral-temporal synthetic remote sensing image is produced.