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Supervised spatial classification of multispectral LiDAR data in urban areas
摘要： 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%.
关键词： land cover classification,urban areas,morphological profiles,spatial classification,multispectral LiDAR
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fusion of Lidar, Hyperspectral and RGB Data for Urban Land Use and Land Cover Classification
摘要： In this paper, we present an ensemble-based classification approach for urban land use and land cover classification based on multispectral LiDAR, hyperspectral and very high resolution RGB data. The approach has been evaluated on the dataset provided for the IEEE GRSS 2018 Data Fusion Contest organized by the GRSS IADF technical committee and has been proven to have a high operational performance, being able to distinguish between different grass-, building- and street-types among other classes like water, railways and parking lots as well as other non-typical classes like cars, trains, stadium seats, etc.
关键词： multispectral LiDAR,very high-resolution RGB,hyperspectral imaging,land use classification
更新于2025-09-10 09:29:36
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Variability of Multispectral Lidar 3D and Intensity Features with Individual Tree Height and Its Influence on Needleleaf Tree Species Identification
摘要： Tree species identification is important in forest management. The multispectral lidar Titan of Teledyne Optech Inc. can improve tree species separation by providing classification features computed from the three-channel intensities, ratios and normalized differences. However, the value of features used in classification algorithms (e.g., random forest, RF) may vary with tree size. The focus of the present study is to show how tree height influences the 3D and intensity features, how this relationship may affect the species classification accuracy, and how different classification strategies may circumvent this problem. Six needleleaf species (Pinus resinosa, Pinus strobus, Pinus sylvestris, Larix laricina, Picea abies and Picea glauca), found in plantations of different ages, were sampled to train classifiers. Some features yielded a good discriminatory power for species identification, despite their relation to tree height (r2 up to 0.6). Two classification strategies—a) using only size-invariant features (SIF) and b) training separate classifiers per tree height strata (HSC)—were compared to a standard classification (STD: all features, without height stratification). The accuracy of the SIF approach was lowest, useful variables being removed due to their relationship to tree height. The HSC provided only a minor improvement over the STD results.
关键词： tree species identification,Teledyne Optech Inc.,Titan,random forest,3D features,intensity features,multispectral lidar
更新于2025-09-04 15:30:14
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Wavelength selection of the multispectral lidar system for estimating leaf chlorophyll and water contents through the PROSPECT model
摘要： The estimation of leaf biochemical constituents is of high interest for the physiological and ecological applications of remote sensing. The multispectral lidar (MSL) system emerges as a promising active remote sensing technology with the ability to acquire both three-dimensional and spectral characteristics of targets. The detection wavelengths of the MSL system can be geared toward the specific application purposes. Therefore, it’s important to conduct the wavelength selection work to maximize the potential of the MSL system in vegetation monitoring. Traditional strategies of wavelength selection attempt to establish an empirical relationship between large quantities of observed reflectance and foliar biochemical constituents. By contrast, this study proposed to select wavelengths through the radiative transfer model PROSPECT. A five-wavelength combination was established to estimate leaf chlorophyll and water contents: 680, 716, 1104, 1882 and 1920 nm. The consistency of the wavelengths selected were tested by running different versions of PROSPECT model. Model inversion using simulated and experimental datasets showed that the selected wavelengths have the ability to retrieve leaf chlorophyll and water contents accurately. Overall, this study demonstrated the potential of the MSL system in vegetation monitoring and can serve as a guide in the design of new MSL systems for the application community.
关键词： Multispectral lidar,Wavelength selection,Leaf water content,Leaf chlorophyll content,PROSPECT model
更新于2025-09-04 15:30:14