Comparison of Boreal Biomass Estimations Using C- and X-Band Polsar

Persson, Henrik J.; Mukhopadhyay, Ritwika; Huuva, Ivan; Fransson, Johan E. S.

doi:10.1109/igarss46834.2022.9884506

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Since 2000, data from Very high spatial resolution (VHR) commercial satellite sensors have shown the potential to create digital base maps [32], and images acquired by VHR sensors enable the successful classification of tree species [33][34][35][36][37][38][39][40]. Typical VHR satellite sensors include GeoEye-1, Gaofen-2, IKONOS, Quickbird, Plé-aides, RapidEye and WorldView-2/3/4 (WV2/3/4).…”

Section: Significance Of Tree Species Informationmentioning

confidence: 99%

A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-based Methods

Zhong,

Dai,

Fang

et al. 2024

Preprint

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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.

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