Investigation of band selection techniques to enable classification of hyperspectral imagery at the edge

Processing hyperspectral image data can be computationally expensive and difficult to employ for real-time applications due to its extensive spatial and spectral information. Further, applications in which computational resources may be limited, such as those requiring artificial intelligence at the edge, can be hindered by the volume of data that is common with airborne hyperspectral image data. This paper proposes utilizing band selection to down-select the number of spectral bands considering a given classification task so that classification can be done at the edge with lower computational complexity. Specifically, we consider popular techniques for band selection and investigate their feasibility to identify discriminative bands such that classification performance is not drastically hindered. This would greatly benefit applications where time-sensitive solutions are needed to ensure optimal outcomes (this could be related to defense, natural disaster relief/response, agriculture, etc.). Performance of the proposed approach is measured in terms of classification accuracy and run time.