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