Deep Convolutional Compressed Sensing-Based Adaptive 3D Reconstruction of Sparse LiDAR Data: A Case Study for Forests

Deep Convolutional Compressed Sensing-Based Adaptive 3D Reconstruction of Sparse LiDAR Data: A Case Study for Forests

LiDAR point clouds are characterized by high geometric and radiometric resolution and are therefore of great use for large-scale forest analysis. Although the analysis of 3D geometries and shapes has improved at different resolutions, processing large-scale 3D LiDAR point clouds is difficult due to...

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Bibliographic Details
Main Authors: Rajat C. Shinde, Surya S. Durbha
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Remote Sensing
Subjects:
data-centric AI
data-driven 3D reconstruction
LiDAR for forests
multivariate empirical mode decomposition
sparse representation
convolutional neural networks (CNN)
Online Access:https://www.mdpi.com/2072-4292/15/5/1394

Internet

https://www.mdpi.com/2072-4292/15/5/1394

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