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Related Experiment Video

Updated: Jul 14, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

More appropriate DenseNetBL classifier for small sample tree species classification using UAV-based RGB imagery.

Ni Wang¹, Tao Pu^1,2, Yali Zhang¹

¹School of Geographic Information and Tourism, Chuzhou University, Chuzhou, 23900, China.

|October 9, 2023

Summary

A new method, DenseNetBL, effectively classifies tree species with limited data. DenseNetBL outperforms other models, especially in small-sample scenarios, enabling accurate tree species area extraction.

Keywords:

DenseNetBL Forests tree species classification SLIC Small sample classification

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Area of Science:

Computer Vision
Machine Learning
Remote Sensing

Background:

Accurate tree species classification is crucial for forest management and ecological studies.
Limited sample sizes pose a significant challenge for developing robust classification models.

Purpose of the Study:

To introduce and evaluate DenseNetBL, a novel approach for tree species classification using limited datasets.
To assess the performance of DenseNetBL in tree species area extraction.

Main Methods:

Developed DenseNetBL by integrating DenseNet architecture with a bottleneck layer.
Evaluated DenseNetBL on small-sample tree species data, remote sensing datasets, and compared it with state-of-the-art classifiers.
Quantified tree species areas by comparing pixel areas from manual and classifier-generated maps.

Main Results:

DenseNetBL outperformed its DenseNet counterpart without pre-trained weights.
DenseNetBL showed superior performance in small-sample classification compared to Swin Transformer and Vision Transformer.
DenseNet33BL achieved the highest accuracy (OA=0.901, Kappa=0.892) in small-sample classification.
DenseNet33BL combined with clustering provided optimal tree species area extraction.

Conclusions:

DenseNetBL is an effective approach for small-sample tree species classification.
The proposed method significantly improves tree species area extraction accuracy.
DenseNetBL offers a promising solution for challenges in remote sensing-based forest inventories.