Classification of Lidar Data Using Standard Deviation of Elevation and Characteristic Point Features

Amolins, Krista; Zhang, Yun; Dare, Peter

doi:10.1109/igarss.2008.4779133

Cited by 3 publications

(1 citation statement)

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“…The availability of the Light Detection and Ranging (LiDAR) system succeeds to map vertical structure of surface objects. The LiDAR systems emit laser pulses using the wavelength in near infrared and record the returned laser pulse signals after backscattered from the targets [17][18][19]. The LiDAR system plays an increasingly vital role in the urban land cover mapping due to its power to acquire the vertical structure of surface objects with high positional accuracy.…”

Section: Introductionmentioning

confidence: 99%

Mapping Urban Land Cover of a Large Area Using Multiple Sensors Multiple Features

Chen

et al. 2018

Remote Sensing

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Concerning the strengths and limitations of multispectral and airborne LiDAR data, the fusion of such datasets can compensate for the weakness of each other. This work have investigated the integration of multispectral and airborne LiDAR data for the land cover mapping of large urban area. Different LiDAR-derived features are involoved, including height, intensity, and multiple-return features. However, there is limited knowledge relating to the integration of multispectral and LiDAR data including three feature types for the classification task. Furthermore, a little contribution has been devoted to the relative importance of input features and the impact on the classification uncertainty by using multispectral and LiDAR. The key goal of this study is to explore the potenial improvement by using both multispectral and LiDAR data and to evaluate the importance and uncertainty of input features. Experimental results revealed that using the LiDAR-derived height features produced the lowest classification accuracy (83.17%). The addition of intensity information increased the map accuracy by 3.92 percentage points. The accuracy was further improved to 87.69% with the addition multiple-return features. A SPOT-5 image produced an overall classification accuracy of 86.51%. Combining spectral and spatial features increased the map accuracy by 6.03 percentage points. The best result (94.59%) was obtained by the combination of SPOT-5 and LiDAR data using all available input variables. Analysis of feature relevance demonstrated that the normalized digital surface model (nDSM) was the most beneficial feature in the classification of land cover. LiDAR-derived height features were more conducive to the classification of urban area as compared to LiDAR-derived intensity and multiple-return features. Selecting only 10 most important features can result in higher overall classification accuracy than all scenarios of input variables except the feature of entry scenario using all available input features. The variable importance varied a very large extent in the light of feature importance per land cover class. Results of classification uncertainty suggested that feature combination can tend to decrease classification uncertainty for different land cover classes, but there is no "one-feature-combination-fits-all" solution. The values of classification uncertainty exhibited significant differences between the land cover classes, and extremely low uncertainties were revealed for the water class. However, it should be noted that using all input variables resulted in relatively lower classification uncertainty values for most of the classes when compared to other input features scenarios.

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Section: Introductionmentioning

confidence: 99%