Unsupervised Classification of Raw Full-Waveform Airborne Lidar Data by Self Organizing Maps

2015 ◽  
pp. 62-72 ◽  
Author(s):  
Eleonora Maset ◽  
Roberto Carniel ◽  
Fabio Crosilla
Keyword(s):  
Unsupervised Classification ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Self Organizing Maps ◽  
Full Waveform ◽  
Airborne Lidar Data ◽  
Self Organizing

Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps

Survey Review ◽  
2018 ◽  
Vol 52 (371) ◽  
pp. 150-158
Author(s):  
Alper Sen ◽  
Baris Suleymanoglu ◽  
Metin Soycan
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Self Organizing Maps ◽  
Airborne Lidar Data ◽  
Self Organizing

scholarly journals A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

2021 ◽  
Vol 13 (4) ◽  
pp. 559
Author(s):  
Milto Miltiadou ◽  
Neill D. F. Campbell ◽  
Darren Cosker ◽  
Michael G. Grant
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Airborne Lidar ◽  
Memory Allocation ◽  
Lidar Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Airborne Lidar Data ◽  
Full Waveform Lidar ◽  
Polygonal Model

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

K-Plane-Based Classification of Airborne LiDAR Data for Accurate Building Roof Measurement

2014 ◽  
Vol 63 (5) ◽  
pp. 1200-1214 ◽  
Author(s):  
Deming Kong ◽  
Lijun Xu ◽  
Xiaolu Li ◽  
Shuyang Li
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Building Roof ◽  
Airborne Lidar Data

scholarly journals Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

2017 ◽  
Vol 9 (8) ◽  
pp. 771 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Dunyong Zheng ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data

A Two-Phase Classification of Urban Vegetation Using Airborne LiDAR Data and Aerial Photography

2014 ◽  
Vol 7 (10) ◽  
pp. 4153-4166 ◽  
Author(s):  
Xiaohua Tong ◽  
Xiaoichun Li ◽  
Xiong Xu ◽  
Huan Xie ◽  
Tiantian Feng ◽  
...  
Keyword(s):  
Aerial Photography ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Urban Vegetation ◽  
Two Phase ◽  
Phase Classification ◽  
Airborne Lidar Data

Full-Waveform Airborne LiDAR Data Classification Using Convolutional Neural Networks

2019 ◽  
Vol 57 (10) ◽  
pp. 8255-8261 ◽  
Author(s):  
Stefano Zorzi ◽  
Eleonora Maset ◽  
Andrea Fusiello ◽  
Fabio Crosilla
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Classification ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Full Waveform ◽  
Airborne Lidar Data

scholarly journals Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels

2020 ◽  
Vol 12 (12) ◽  
pp. 2048
Author(s):  
Charlie Marshak ◽  
Marc Simard ◽  
Laura Duncanson ◽  
Carlos Alberto Silva ◽  
Michael Denbina ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Single Model ◽  
Tropical Regions ◽  
Full Waveform ◽  
Applicable Range ◽  
L Band ◽  
Airborne Lidar Data ◽  
Synergistic Relationships

We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.

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