Parking lot process model incorporated into DIRSIG scene simulation

Abstract

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool is a rst 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 20mm, it produces multi or hyperspectral remote sensing images. By integrating independent rst principles based sub-models, such as MODTRAN, DIRSIG generates a representation of what a sensor would see with high radiometric delity. In order to detect temporal changes in a process within the scene, currently the eort is devoted to enhance the capacity of DIRSIG by incorporating process models. The parking lot process model is interesting to many applications. Therefore, this paper builds a parking lot process model PARKVIEW based on the statistical description of the parking lot which includes parking lot occupancy, parking duration and parking spot preference. The output of PARKVIEW could then be fed into DIRSIG to enhance the scene simulation capacity of DIRSIG in terms of including temporal information of the parking lot. In order to show an accurate and ecient way of extracting the statistical description of the parking lot, an experiment is set up to record the distribution of cars in several parking lots on the RIT campus during one weekday by taking photos every ve minutes. The image data are processed to extract the parking spot status of the parking lot for each frame taken from the experiment. The parking spot status information is then described in a statistical way.