Prediction of forest canopy structure from PolInSAR dataset

Brigot, Guillaume; Simard, Marc; Colin-Koeniguer, Élise; Taillandier, Cédric

doi:10.1109/igarss.2017.8127954

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017

DOI: 10.1109/igarss.2017.8127954

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Prediction of forest canopy structure from PolInSAR dataset

Guillaume Brigot

Marc Simard

Élise Colin-Koeniguer³

et al.

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“…The mapping of PolInSAR parameters into LIDAR labels cannot be represented as a simple function. Although, SVMs have been used in similar approaches with encouraging results [35], we obtained higher accuracy with neural networks. • A significant practical advantage of a perceptron over SVMs is that perceptrons can be trained on-line, with their weights updated as new examples are provided.…”

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confidence: 72%

Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

et al. 2019

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This paper presents a machine learning based method to predict the forest structure parameters from L-band polarimetric and interferometric synthetic aperture radar (PolInSAR) data acquired by the airborne UAVSAR system over the Réserve Faunique des Laurentides in Québec, Canada. The main objective of this paper is to show that relevant parameters of the PolInSAR coherence region can be used to invert forest structure indicators computed from the airborne LIDAR sensor Laser Vegetation and Ice Sensor (LVIS). The method relies on the shape of the observed generalized PolInSAR coherence region that is related to the three-dimensional structure of the scene. In addition to parameters describing the coherence shape, we consider the impact of acquisition parameters such as the interferometric baseline, ground elevation and local surface slope. We use the parameters as input a multilayer perceptron model to infer canopy features as estimated from LIDAR waveform. The output features are canopy height, cover and vertical profile class. Canopy height and canopy cover are estimated with a normalized RMSE of 13%, 15% respectively. The vertical profile was divided into 3 distinct classes with 66% accuracy.

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Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

et al. 2019

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Compact Polarimetric SAR Interferometry Target Decomposition With the Freeman–Durden Method

Han

Jiang

Wang

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Machine-Learning Inversion of Forest Vertical Structure Based on 2-D-SGVBVoG Model for P-Band Pol-InSAR

Sun

Wang

Xiang

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Prediction of forest canopy structure from PolInSAR dataset

Cited by 3 publications

References 5 publications

Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

Compact Polarimetric SAR Interferometry Target Decomposition With the Freeman–Durden Method

Machine-Learning Inversion of Forest Vertical Structure Based on 2-D-SGVBVoG Model for P-Band Pol-InSAR

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