Tree Species Classification in UAV Remote Sensing Images Based on Super-Resolution Reconstruction and Deep Learning

Abstract

We studied the use of self-attention mechanism networks (SAN) and convolutional neural networks (CNNs) for forest tree species classification using unmanned aerial vehicle (UAV) remote sensing imagery in Dongtai Forest Farm, Jiangsu Province, China. We trained and validated representative CNN models, such as ResNet and ConvNeXt, as well as the SAN model, which incorporates Transformer models such as Swin Transformer and Vision Transformer (ViT). Our goal was to compare and evaluate the performance and accuracy of these networks when used in parallel. Due to various factors, such as noise, motion blur, and atmospheric scattering, the quality of low-altitude aerial images may be compromised, resulting in indistinct tree crown edges and deficient texture. To address these issues, we adopted Real-ESRGAN technology for image super-resolution reconstruction. Our results showed that the image dataset after reconstruction improved classification accuracy for both the CNN and Transformer models. The final classification accuracies, validated by ResNet, ConvNeXt, ViT, and Swin Transformer, were 96.71%, 98.70%, 97.88%, and 98.59%, respectively, with corresponding improvements of 1.39%, 1.53%, 0.47%, and 1.18%. Our study highlights the potential benefits of Transformer and CNN for forest tree species classification and the importance of addressing the image quality degradation issues in low-altitude aerial images.

Mapping the Distribution of High-Value Broadleaf Tree Crowns through Unmanned Aerial Vehicle Image Analysis Using Deep Learning

“…Regarding data augmentation, the former authors have discussed that this technique was able to increase the accuracy of the classification results for tree species classification [5,42,46]. By contrast, in our study, the augmented September and October UAV datasets either did not improve or decreased the accuracy of Japanese oak crown detection, as evaluated in terms of OA, recall, F1-score, and IoU values (Tables 1 and 2).…”

“…The traditional method of acquiring this information is based on individual tree species and thus time-consuming and laborious [5,6], as well as difficult to apply on complex uneven-mixed forests [7]. The recent development of remote sensing techniques has provided an effective and efficient means of obtaining highly accurate forest information [6].…”

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

“…The computational operations performed by these convolutional layers effectively model spatial relationships within the images, allowing ResNet50 to discern different levels of detail in agricultural land cover. Consequently, it excels in capturing deep features related to crop surface coverage with enhanced proficiency [58]. These deep features mitigate the estimation errors caused by the complex canopy structure of the environment and crops.…”

Generating 10-Meter Resolution Land Use and Land Cover Products Using Historical Landsat Archive Based on Super Resolution Guided Semantic Segmentation Network

“…It was conducted in two stages: image super-resolution preprocessing and LULC classification. Huang, et al [19] employed Enhanced Super-Resolution Generative Adversarial Networks for super-resolution image construction, subsequently integrating semantic segmentation models for the classification of tree species. Since restored images with better super resolution quality do not always guarantee better semantic segmentation results and super-resolution and semantic segmentation can promote each other [20], end-to-end approaches with interaction between these two tasks are introduced.…”