Version 1
: Received: 7 April 2024 / Approved: 8 April 2024 / Online: 8 April 2024 (15:05:04 CEST)
How to cite:
Zhong, L.; Dai, Z.; Fang, P.; Cao, Y.; Wang, L. A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-based Methods. Preprints2024, 2024040569. https://doi.org/10.20944/preprints202404.0569.v1
Zhong, L.; Dai, Z.; Fang, P.; Cao, Y.; Wang, L. A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-based Methods. Preprints 2024, 2024040569. https://doi.org/10.20944/preprints202404.0569.v1
Zhong, L.; Dai, Z.; Fang, P.; Cao, Y.; Wang, L. A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-based Methods. Preprints2024, 2024040569. https://doi.org/10.20944/preprints202404.0569.v1
APA Style
Zhong, L., Dai, Z., Fang, P., Cao, Y., & Wang, L. (2024). A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-based Methods. Preprints. https://doi.org/10.20944/preprints202404.0569.v1
Chicago/Turabian Style
Zhong, L., Yong Cao and Leiguang Wang. 2024 "A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-based Methods" Preprints. https://doi.org/10.20944/preprints202404.0569.v1
Abstract
Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.
Keywords
remote sensing; tree species classification; unimodal and multimodal remote sensing data; classic deep learning-based methods
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
