Leveraging Hyperspectral Images for Accurate Insect Classification with a Novel Two-Branch Self-Correlation Approach

Abstract

Insect recognition, crucial for agriculture and ecology studies, benefits from advancements in RGB image-based deep learning, yet still confronts accuracy challenges. To address this gap, the HI30 dataset is introduced, comprising 2115 hyperspectral images across 30 insect categories, which offers richer information than RGB data for enhancing classification accuracy. To effectively harness this dataset, this study presents the Two-Branch Self-Correlation Network (TBSCN), a novel approach that combines spectrum correlation and random patch correlation branches to exploit both spectral and spatial information. The effectiveness of the HI30 and TBSCN is demonstrated through comprehensive testing. Notably, while ImageNet-pre-trained networks adapted to hyperspectral data achieved an 81.32% accuracy, models developed from scratch with the HI30 dataset saw a substantial 9% increase in performance. Furthermore, applying TBSCN to hyperspectral data raised the accuracy to 93.96%. Extensive testing confirms the superiority of hyperspectral data and validates TBSCN’s efficacy and robustness, significantly advancing insect classification and demonstrating these tools’ potential to enhance precision and reliability.