Remote Sens. 2014, 6(1), 700-715; doi:10.3390/rs6010700

Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover

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Received: 8 October 2013; in revised form: 16 December 2013 / Accepted: 31 December 2013 / Published: 8 January 2014
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: This study demonstrated the potential of using dual-wavelength airborne light detection and ranging (LiDAR) data to classify land cover. Dual-wavelength LiDAR data were acquired from two airborne LiDAR systems that emitted pulses of light in near-infrared (NIR) and middle-infrared (MIR) lasers. The major features of the LiDAR data, such as surface height, echo width, and dual-wavelength amplitude, were used to represent the characteristics of land cover. Based on the major features of land cover, a support vector machine was used to classify six types of suburban land cover: road and gravel, bare soil, low vegetation, high vegetation, roofs, and water bodies. Results show that using dual-wavelength LiDAR-derived information (e.g., amplitudes at NIR and MIR wavelengths) could compensate for the limitations of using single-wavelength LiDAR information (i.e., poor discrimination of low vegetation) when classifying land cover.
Keywords: dual-wavelength; LiDAR; land cover classification; support vector machine (SVM)
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MDPI and ACS Style

Wang, C.-K.; Tseng, Y.-H.; Chu, H.-J. Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover. Remote Sens. 2014, 6, 700-715.

AMA Style

Wang C-K, Tseng Y-H, Chu H-J. Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover. Remote Sensing. 2014; 6(1):700-715.

Chicago/Turabian Style

Wang, Cheng-Kai; Tseng, Yi-Hsing; Chu, Hone-Jay. 2014. "Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover." Remote Sens. 6, no. 1: 700-715.

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