Design and Demonstration of a Lattice-Based Target for Hyperspectral Subpixel Target Detection Experiments

Abstract

A lattice-based target design is presented for expanding research capabilities in subpixel target detection. The targets generate large numbers of subpixel samples with a priori knowledge of the exact subpixel fractions. This contrasts with traditional targets, where subpixel fractions are either unknown or estimated with significant uncertainty, with limited samples available in historical datasets. The subpixel targets diminish these drawbacks, and generate constant subpixel samples invariant to effects of the system (e.g. image distortions, scan pattern) which would typically induce uncertainty. Simulations were performed to assess the accuracy of the proposed method of achieving samples with constant fractions. To validate and demonstrate the functionality of the design, four targets were fabricated with constant subpixel fractions (0.2, 0.4, 0.6, 0.8) and were deployed into a hyperspectral data collection. Spectral unmixing validated the retrieval of samples with constant fractions, and a general target detection scenario was demonstrated using 300 − 400 samples of each constant fraction. The impacts of a limited number of target samples (e.g. n = 5, 10) on ROC curves were empirically assessed, with significant reduction of variability observed when n > 100, illustrating the advantages when large sample sizes are available. Design limitations are discussed, along with applications (e.g. algorithm comparison) for the community.