3d Land Cover Classification Based on Multispectral Lidar Point Clouds

Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

doi:10.5194/isprsarchives-xli-b1-741-2016

Cited by 17 publications

(13 citation statements)

References 3 publications

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“…Fernandez-Diaz et al (2016) [20] presented the capabilities of assessment and performance metrics for the multi-wavelength LiDAR to quantify the performance of Optech Titan multi-wavelength LiDAR system in land cover classification, bathymetric mapping, canopy characterization, and geometrical accuracy. Zou et al (2016) [21] used Optech Titan multi-wavelength LiDAR data to perform object-based land cover classification and found that a pseudo normalized difference vegetation index (pNDVI) generated from a multi-wavelength LiDAR system may improve vegetation identification, achieving an overall accuracy higher than 90% and kappa coefficient reaching 0.89. Sun et al (2017) [22] compared the reflectance of active multispectral LiDAR, active hyperspectral LiDAR, and passive spectrometer for leaf nitrogen concentration; their coefficient of determination (R 2 ) for spectrometer (R 2 = 0.73) and hyperspectral LiDAR (R 2 = 0.74) showed a high correlation with leaf nitrogen content.…”

Section: Previous Studiesmentioning

confidence: 99%

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Teo

2017

Applied Sciences

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Featured Application: The featured application of this study is to combine the geometrical and spectral features from advanced multi-wavelength LiDAR in land cover classification. Abstract:The airborne multi-wavelength light detection and ranging (LiDAR) system measures different wavelengths simultaneously and usually includes two or more active channels in infrared and green to acquire both topographic and hydrographic information. The reflected multi-wavelength energy can also be used to identify different land covers based on physical properties of materials. This study explored the benefits of multi-wavelength LiDAR in object-based land cover classification, focusing on three major issues: (1) the evaluation of single-and multi-wavelength LiDARs for land cover classification; (2) the performance of spectral and geometrical features extracted from multi-wavelength LiDAR; and (3) the comparison of the vegetation index derived from active multi-wavelength LiDAR and passive multispectral images. The three-wavelength test data were acquired by Optech Titan in green, near-infrared, and mid-infrared channels, and the reference data were acquired from Worldview-3 image. The experimental results show that the multi-wavelength LiDAR provided higher accuracy than single-wavelength LiDAR in land cover classification, with an overall accuracy improvement rate about 4-14 percentage points. The spectral features performed better compared to geometrical features for grass, road, and bare soil classes, and the overall accuracy improvement is about 29 percentage points. The results also demonstrated the vegetation indices from Worldview-3 and Optech Titan have similar characteristics, with correlations reaching 0.68 to 0.89. Overall, the multi-wavelength LiDAR system improves the accuracy of land cover classification because this system provides more spectral information than traditional single-wavelength LiDAR.

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Section: Previous Studiesmentioning

confidence: 99%

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Teo

2017

Applied Sciences

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“…Raster images were created from the LiDAR intensity and height data, and image classification techniques were then applied [35][36][37][38]. As well, the multispectral LiDAR data were explored for 3D point classification in urban areas [39][40][41][42].…”

Section: Multispectral Lidar Systemsmentioning

confidence: 99%

Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada

2018

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Coastal areas are environmentally sensitive and are affected by nature events and human activities. Land/water interaction in coastal areas changes over time and, therefore, requires accurate detection and frequent monitoring. Multispectral Light Detection and Ranging (LiDAR) systems, which operate at different wavelengths, have become available. This new technology can provide an effective and accurate solution for the determination of the land/water interface. In this context, we aim to investigate a set of point features based on elevation, intensity, and geometry for this application, followed by a presentation of an unsupervised land/water discrimination method based on seeded region growing algorithm. The multispectral airborne LiDAR sensor, the Optech Titan, was used to acquire LiDAR data at three wavelengths (1550, 1064, and 532 nm) of a study area covering part of Lake Ontario in Scarborough, Canada for testing the discrimination methods. The elevationand geometry-based features achieved an average overall accuracy of 75.1% and 74.2%, respectively, while the intensity-based features achieved 63.9% accuracy. The region growing method succeeded in discriminating water from land with more than 99% overall accuracy, and the land/water boundary was delineated with an average root mean square error of 0.51 m. The automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength.

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“…New multispectral LiDAR data sensors (e.g., Multispectral Optech Titan LiDAR), which measures backscattered energies at different wavelengths, provide new opportunities to classify urban land cover effectively [ 30 ]. Since the release of the first commercial airborne multispectral LiDAR system, several studies have been tested to assess capabilities to produce more accurate land cover maps [ 29 – 32 , 33 – 37 ]. For instance, Teo et al demonstrated that multi-wavelength LiDAR can provide higher accuracy than single-wavelength LiDAR for land cover classification [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…Fernandez-Diaz et al [ 36 ] assessed capabilities of the Titan multispectral LiDAR for land cover classification, bathymetric mapping and canopy characterization. Zou et al [ 37 ] adopted the object-based method and found that pseudo normalized difference vegetation index (pseudoNDVI) calculated from multispectral LiDAR may improve vegetation identification. In another study [ 33 ], an object-based analysis was also performed on multispectral airborne LiDAR data for land cover classification and map updating in Finland.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by previous studies [ 29 , 33 – 37 ], this research is focused on classification of the multispectral LiDAR intensity rasters, sinceraster format is a more convenient than point clouds for land cover mapping. Previous studies have shown that multispectral LiDAR performs better than single band LiDAR in land cover classification [ 29 ], and found pseudoNDVI [ 37 , 38 ] and DSM [ 34 , 35 ] to be useful for improving classification accuracy of multispectral LiDAR data. in the novelty of this studyis that we investigated the role of spatial information in improving urban land cover classification results employing multispectral LiDAR intensity data (i.e., spectral-spatial classification [ 39 ]).…”

Section: Introductionmentioning

confidence: 99%

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Supervised spatial classification of multispectral LiDAR data in urban areas

et al. 2018

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Multispectral LiDAR (light detection and ranging) data have been initially used for land cover classification. However, there are still high classification uncertainties, especially in urban areas, where objects are often mixed and confounded. This study investigated the efficiency of combining advanced statistical methods and LiDAR metrics derived from multispectral LiDAR data for improving land cover classification accuracy in urban areas. The study area is located in Oshawa, Ontario, Canada, on the Lake Ontario shoreline. Multispectral Optech Titan LiDAR data over the study area were acquired on 3 September 2014 in a single strip of 3 km2. Using the channels at 1,550 nm (C1), 1,064 nm (C2) and 532 nm (C3), LiDAR intensity data, normalized digital surface model (nDSM), pseudo normalized difference vegetation index (PseudoNDVI), morphological profiles (MP), and a novel hierarchical morphological profiles (HMP) were derived and used as features for the classification. A support vector machine classifier with a radial basis function (RBF) kernel was applied in the classification stage, where the optimal parameters for the classifier were selected by a grid search procedure. The combination of intensity, pseudoNDVI, nDSM and HMP resulted in the best land cover classification, with an overall accuracy of 93.28%.

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3d Land Cover Classification Based on Multispectral Lidar Point Clouds

Cited by 17 publications

References 3 publications

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System

Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada

Supervised spatial classification of multispectral LiDAR data in urban areas

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