Spectral Haar Binarizer: Compact Boolean Contrast Representations for Hyperspectral Classification

Abstract

Tsetlin Machines offer an interpretable, hardware-friendly alternative to conventional machine learning models, but applying them to continuous hyperspectral measurements requires converting spectral signals into Boolean representations. Standard binarisation methods typically discretise each spectral band independently, potentially discarding important relationships between neighbouring wavelengths. In this paper, we introduce the Spectral Haar Binarizer (SHB), a contrast-based spectral representation method that constructs Boolean predicates from differences between selected spectral intervals. Inspired by Haar-like contrast features, the method uses an AdaBoost-style selection procedure to obtain a compact set of discriminative spectral predicates. We evaluate SHB on five heterogeneous hyperspectral datasets covering forensic blood detection, agricultural and urban remote sensing, and rainforest tree-species classification. Across binary and multiclass tasks, SHB consistently outperforms standard per-band binarisation strategies and provides the strongest or near-strongest results across classifiers. In binary tasks, TM-SHB achieves the best average performance, while RF-SHB is strongest in multiclass settings. Bayesian and rank-based analyses confirm that SHB's advantage is robust across datasets and not driven by isolated cases. The results show that representing continuous hyperspectral measurements is a central processing step for efficient classification. That contrast-based Boolean features provide an effective bridge between information-rich sensor measurements and compact, logic-based processing.