Hyperspectral to multispectral: optimal selection of mission-relevant bands using machine learning

Abstract

This paper presents a machine-learning-informed optimization approach for designing the most cost-effective multispectral system capable of detecting any arbitrarily selected set of materials. The approach presented accepts from the user a list of entities that need to be detected; it then outputs (a) a short list of band centers and bandwidths required for detecting the entities of interest as well as (b) a collection of trained machine-learning models capable of performing those detections with high accuracy. This approach has the potential to help identify cost savings during the design process by allowing proposed hyperspectral systems to be replaced by bespoke multispectral ones – thereby reducing overall mission costs without sacrificing mission performance. A hypothetical design study demonstrates how the proposed approach can automatically design a six-band multispectral system whose detection capabilities are nearly indistinguishable from those of an 80-band hyperspectral system. More precisely, the design procedure was able to reduce the number of required bands by over 90% while only seeing a 0.5% decrease in the average F1 score of a set of machine-learning models trained to identify 26 polymeric materials of interest.