# Research on Intelligent Wood Species Identification Method Based on Multimodal Texture-Dominated Features and Deep Learning Fusion

## TL;DR

This paper introduces a deep learning method that uses texture and spectral features to accurately identify wood species, improving on traditional manual methods.

## Contribution

A novel fusion method combining multimodal texture-dominated features and deep learning for wood species identification is proposed.

## Key findings

- The proposed joint model achieved an overall classification accuracy of 90.27%.
- The model outperformed single-modal approaches by approximately 8% on average.
- The method uses hyperspectral imaging and deep learning fusion for robust wood identification.

## Abstract

Aimed at the problems of traditional wood species identification relying on manual experience, slow identification speed, and insufficient robustness, this study takes hyperspectral images of cross-sections of 10 typical wood species commonly found in Puer, Yunnan, China, as the research object. It comprehensively applies various spectral and texture feature extraction technologies and proposes an intelligent wood species identification method based on the fusion of multimodal texture-dominated features and deep learning. Firstly, an SOC710-VP hyperspectral imager is used to collect hyperspectral data under standard laboratory lighting conditions, and a hyperspectral database of wood cross-sections is constructed through reflectance calibration. Secondly, in the spectral space construction stage, a comprehensive similarity matrix is built based on four types of spectral similarity indicators. Representative bands are selected using two Max–Min strategies: partitioned quota and coverage awareness. Multi-scale wavelet fusion is performed to generate high-resolution fused images and extract interest point features. Thirdly, in the texture space construction stage, three types of texture feature matrices are generated based on the PCA first principal component map, and interest point features are extracted. Fourthly, in the complementary collaborative learning stage, the ST-former model is constructed. The weights of the trained SpectralFormer++ and TextureFormer are imported, and only the fusion weights are optimized and learned to realize category-adaptive spectral–texture feature fusion. Experimental results show that the overall classification accuracy of the proposed joint model reaches 90.27%, which is about 8% higher than that of single-modal models on average.