A 3D-2DCNN-CA approach for enhanced classification of hickory tree species using UAV-based hyperspectral imaging

Abstract

Hickory trees possess substantial economic, nutritional, and ecological value, and they play a pivotal role in both human societies and natural ecosystems. To maximize benefits, many bases have adopted a composite planting strategy. Effective and timely monitoring of hickory forest species information is crucial for precise management and conservation. In this study, a low-altitude unmanned aerial vehicle (UAV) was employed to capture RGB and hyperspectral data from the canopy of two hickory bases. A hybrid convolutional neural network structure was then utilized to classify different tree species and congeneric hickory species. By introducing a channel attention module to refine the features of hyperspectral images, classification stability was enhanced. The experimental results demonstrate that hyperspectral images yield superior classification performance in hickory species identification compared to RGB images, particularly in classifying highly homogeneous tree species. The 3D-2DCNN-CA proposed in this paper demonstrated the best performance in classifying hickory species compared to other classification methods. In the classification of different tree species, the accuracy for a single hickory reached 99.38%, and in the classification of the same hickory species, it achieved an accuracy of 93.97%. Furthermore, this method also achieved significant classification results at the single-tree scale. These results indicate that the method can realize fine-scale monitoring of hickory forests and provide substantial support for forest land management and expert guidance on planting distribution.