Assessing the impact of spectral and polarimetric data fusion via simulation to support multimodal sensor system design requirements

Abstract

A series of trade studies was carried out using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model to assess how varying the spectral signal-to-noise ratio (SNR), spectral ground sample distance (GSD), or target spectrum affected the impact of spectral and polarimetric data fusion via the spectral polarimetric integration (SPI) algorithm for a notional multimodal sensor. When varying the SNR, the impact depended on the constraints placed on the sensor's tasking. When spectral GSD was varied, the benefit of incorporating polarimetric information increased as the GSD increased. However, a threshold GSD was identified beyond which no benefit was observed. Reducing the target/background spectral contrast by changing the target spectrum from a red vehicle to a green vehicle produced variations in the impact due to fusion, although the SPI algorithm produced a general increase in performance in both cases. The trade studies demonstrated that incorporating additional polarimetric information may enable suitable performance with a less capable multispectral sensor. Finally, the SPI decision fusion algorithm was shown to be robust across a range of scenarios possibly encountered in the multimodal sensor design process.