Selecting Hyperspectral Bands for Leaf Mass Per Area Prediction by Means of Neural Networks-Reference-Cited by-同舟云学术

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Selecting Hyperspectral Bands for Leaf Mass Per Area Prediction by Means of Neural Networks

Published:2022-07-17 Issue: Volume: Page:
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Container-title:IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Baskiotis Catherine¹,Aval Josselin¹,Bouz Marwa El¹,Falou Ayman Al¹

Affiliation:

1. L@bISEN, Light Scattering Learning Team, Yncrea Ouest,Brest,France,29200

Funder

Brittany Region (France)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9883023/9883024/09884541.pdf?arnumber=9884541

Reference11 articles.

1. Optimized Estimation of Leaf Mass per Area with a 3D Matrix of Vegetation Indices

2. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass

3. Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements

4. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments

5. Leaf Optical Properties

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Invertible Physics-Based Hyperspectral Signature Models: A review;IEEE Geoscience and Remote Sensing Magazine;2023-12

2. Information extraction in polarization-resolved second harmonic generation microscopy images of human tissues for automatic cancer diagnosis;Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2023;2023-03-15

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