Relevance assessment of full-waveform lidar data for urban area classification

Mallet, Clément; Bretar, Frédéric; Roux, Michel; Soergel, Uwe; Heipke, Christian

doi:10.1016/j.isprsjprs.2011.09.008

Cited by 273 publications

(226 citation statements)

References 39 publications

Supporting

Mentioning

218

Contrasting

Unclassified

Order By: Relevance

“…Among such features, shape measures in particular are often used as they are rather intuitive and represent one single property of the local neighborhood by a single value (West et al, 2004;Jutzi and Gross, 2009;Mallet et al, 2011;Weinmann et al, 2015;Guo et al, 2015).…”

Section: Feature Extractionmentioning

confidence: 99%

“…To reason about specific objects in the scene and use the respective information for modeling or planning processes, many applications rely on a semantic labeling of the acquired point clouds as an initial step. Such a semantic labeling is typically achieved via point cloud classification (Chehata et al, 2009;Shapovalov et al, 2010;Mallet et al, 2011;Niemeyer et al, 2014;Hackel et al, 2016;Weinmann, 2016;Grilli et al, 2017), where the objective is to assign a semantic label to each point of the point cloud.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

Blomley

Weinmann

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:In this paper, we present a novel framework for the semantic labeling of airborne laser scanning data on a per-point basis. Our framework uses collections of spherical and cylindrical neighborhoods for deriving a multi-scale representation for each point of the point cloud. Additionally, spatial bins are used to approximate the topography of the considered scene and thus obtain normalized heights. As the derived features are related with different units and a different range of values, they are first normalized and then provided as input to a standard Random Forest classifier. To demonstrate the performance of our framework, we present the results achieved on two commonly used benchmark datasets, namely the Vaihingen Dataset and the GML Dataset A, and we compare the results to the ones presented in related investigations. The derived results clearly reveal that our framework excells in classifying the different classes in terms of pointwise classification and thus also represents a significant achievement for a subsequent spatial regularization.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

Blomley

Weinmann

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…LiDAR has received much attention in recent years due to its simplicity, high accuracy, and utility in many applications, such as 3D city modeling, cartography, forest inventories, target recognition, and the digital terrain model [6][7][8][9]. To the best of our knowledge, LiDAR can be divided into two categories, i.e., discrete-echo LiDAR and full-waveform LiDAR [10,11]. A discrete-echo LiDAR records only a few discrete-echo backscattered waveform signals for each transmitted laser pulse and provides only 3D coordinates and range information about objects [8,12].…”

Section: Introductionmentioning

confidence: 99%

A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path

et al. 2017

View full text Add to dashboard Cite

A novel de-noising method for improving the performance of full-waveform light detection and ranging (LiDAR) based on differential optical path is proposed, and the mathematical models of this method are developed and verified. Backscattered full-waveform signal (BFWS) is detected by two avalanche photodiodes placed before and after the focus of the focusing lens. On the basis of the proposed method, some simulations are carried out and conclusions are achieved.(1) Background noise can be suppressed effectively and peak points of the BFWS are transformed into negative-going zero-crossing points as stop timing moments. (2) The relative increment percentage of the signal-to-noise ratio based on the proposed method first dramatically increases with the increase of the distance, and then the improvement gets smaller by increasing the distance. (3) The differential Gaussian fitting with the Levenberg-Marquardt algorithm is applied, and the results show that it can decompose the BFWS with high accuracy. (4) The differential distance should not be larger than c/2 × τ rmin , and two variable gain amplifiers can eliminate the inconsistency of two differential beams. The results are beneficial for designing a better performance full-waveform LiDAR.

show abstract

“…These include classification [1,2], object extraction and 3D reconstruction [3], vegetation structure characterization [4,5] and digital elevation model (DEM) generation [6].…”

Section: Introductionmentioning

confidence: 99%

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

2016

View full text Add to dashboard Cite

Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of the range measurements and the possibility of retrieving additional returns from weak and overlapping pulses. In addition, radiometric characteristics of targets, e.g., target cross-section, can also be retrieved from the waveforms. However, waveform restoration and removal of the effect of the emitted system pulse from the returned waveform are critical for precise range measurement, 3D reconstruction and target cross-section extraction. In this paper, a sparsity-constrained regularization approach for deconvolution of the returned lidar waveform and restoration of the target cross-section is presented. Primal-dual interior point methods are exploited to solve the resulting nonlinear convex optimization problem. The optimal regularization parameter is determined based on the L-curve method, which provides high consistency in varied conditions. Quantitative evaluation and visual assessment of results show the superior performance of the proposed regularization approach in both removal of the effect of the system waveform and reconstruction of the target cross-section as compared to other prominent deconvolution approaches. This demonstrates the potential of the proposed approach for improving the accuracy of both range measurements and geophysical attribute retrieval. The feasibility and consistency of the presented approach in the processing of a variety of lidar data acquired under different system configurations is also highlighted.

show abstract

Relevance assessment of full-waveform lidar data for urban area classification

Cited by 273 publications

References 39 publications

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

Using Multi-Scale Features for the 3d Semantic Labeling Of Airborne Laser Scanning Data

A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

Contact Info

Product

Resources

About