Random forest-based land-use classification using multispectral LiDAR data

doi:10.13474/j.cnki.11-2246.2019.0356

Abstract

Abstract: Airborne LiDAR systems can quickly obtain three-dimensional coordinates of ground objects, which has been widely used in topographic mapping, engineering construction, environmental monitoring, and land-cover and land-use classification, and so on. This paper, by means of random forest algorithm, performs land-cover classification using airborne multispectral LiDAR data. The proposed method extracts features from elevation and multispectral images combined by three individual intensity images, performs a backward feature selection according to the variables importance calculated by RF, and finally applies RF to the multispectral images. All experiments are conducted on the Optech Titan multispectral LiDAR data.The experimental results show that RF can achieve a good performance in land-cover classification, and the proposed RF-based backward feature selection method contributes to the improvement of classification by iteratively removing redundancy and related features.

Key words: multispectral LiDAR, random forest, land-cover classification, variable importance, backward feature selection

CLC Number:

P237

CAO Shuang, PAN Suoyan, GUAN Haiyan. Random forest-based land-use classification using multispectral LiDAR data[J]. Bulletin of Surveying and Mapping, 2019, 0(11): 79-84.

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