Classification and extraction of trees and buildings from urban scenes using discrete return LiDAR and aerial color imagery

Bandyopadhyay, Madhurima; Aardt, Jan A. N. van; Cawse‐Nicholson, Kerry

doi:10.1117/12.2015890

Cited by 17 publications

(10 citation statements)

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“…Figure 3 shows the output associated with the point cloud parameters and the impact those parameters have on the point classification output. This approach is similar to that presented by Bandyopadhyay et al where each point in the point cloud was evaluated for its level of flatness or surface variation based on the eigenvalue [4]. Once the surface variation was determined for each point, regions were created based on neighboring points of similar flatness and comparable surface normal vectors.…”

Section: Texas+ Plane Fittingmentioning

confidence: 99%

“…It is also difficult to establish logic-based decisions or parameter thresholds for vegetation given its wide structural and height diversity across the globe. Bandyopadhyay, et al have shown success in delineating vegetation from building structures in lidar derived point clouds in urban environments, but the success is predicated on the existence of coincident RGB imagery [4].…”

Section: Introductionmentioning

confidence: 99%

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Automated feature extraction for 3-dimensional point clouds

et al. 2016

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Light detection and ranging (LIDAR) technology offers the capability to rapidly capture high-resolution, 3-dimensional surface data with centimeter-level accuracy for a large variety of applications. Due to the foliage-penetrating properties of LIDAR systems, these geospatial data sets can detect ground surfaces beneath trees, enabling the production of highfidelity bare earth elevation models. Precise characterization of the ground surface allows for identification of terrain and non-terrain points within the point cloud, and facilitates further discernment between natural and man-made objects based solely on structural aspects and relative neighboring parameterizations. A framework is presented here for automated extraction of natural and man-made features that does not rely on coincident ortho-imagery or point RGB attributes. The TEXAS (Terrain EXtraction And Segmentation) algorithm is used first to generate a bare earth surface from a lidar survey, which is then used to classify points as terrain or non-terrain. Further classifications are assigned at the point level by leveraging local spatial information. Similarly classed points are then clustered together into regions to identify individual features. Descriptions of the spatial attributes of each region are generated, resulting in the identification of individual tree locations, forest extents, building footprints, and 3-dimensional building shapes, among others. Results of the fully-automated feature extraction algorithm are then compared to ground truth to assess completeness and accuracy of the methodology.

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Section: Texas+ Plane Fittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated feature extraction for 3-dimensional point clouds

et al. 2016

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“…The NDVI images were examined, mean and standard deviation values were calculated and a thresholding technique was applied to separate vegetation from other land cover. The intersection of original image with NDVI images produces a vegetation mask image [18]. Ignoring significant areas and capturing the texture property of vegetation only, results in the image shown in Fig.…”

Section: Pre-processingmentioning

confidence: 99%

The Vegetation Extraction and Hierarchical Classification using an IRS-1C LISS III Image

Parveen¹,

Kulkarni²,

Mytri³

2016

IJCA

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“…Previous studies have demonstrated the utility of LiDAR intensity information (along with spectral information from orthophotos or spectral imagery) for tasks such as image classification or determination of the Normalized Difference Vegetation Index (NDVI) [1,2].…”

Section: Introductionmentioning

confidence: 99%

Correlation between lidar-derived intensity and passive optical imagery

et al. 2014

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When LiDAR data are collected, the intensity information is recorded for each return, and can be used to produce an image resembling those acquired by passive imaging sensors. This research evaluated LiDAR intensity data to determine its potential for use as baseline imagery where optical imagery are unavailable. Two airborne LiDAR datasets collected at different point densities and laser wavelengths were gridded and compared with optical imagery. Optech Orion C200 laser data were compared with a corresponding 1541 nm spectral band from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Optech ALTM Gemini LiDAR data collected at 1064 nm were compared to the WorldView-2 (WV-2) 949 -1043 nm NIR2 band. Intensity images were georegistered and spatially resampled to match the optical data. The Pearson Product Moment correlation coefficient was calculated between datasets to determine similarity. Comparison for the full LiDAR datasets yielded correlation coefficients of approximately 0.5. Because LiDAR returns from vegetation are known to be highly variable, a Normalized Difference Vegetation Index (NDVI) was calculated utilizing the optical imagery, and intensity and optical imagery were separated into vegetation and nonvegetation categories. Comparison of the LiDAR intensity for non-vegetated areas to the optical imagery yielded coefficients greater than 0.9. These results demonstrate that LiDAR intensity data may be useful in substituting for optical imagery where only LiDAR is available.

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Classification and extraction of trees and buildings from urban scenes using discrete return LiDAR and aerial color imagery

Cited by 17 publications

References 0 publications

Automated feature extraction for 3-dimensional point clouds

Automated feature extraction for 3-dimensional point clouds

The Vegetation Extraction and Hierarchical Classification using an IRS-1C LISS III Image

Correlation between lidar-derived intensity and passive optical imagery

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