机载多光谱LiDAR的随机森林地物分类

机载多光谱LiDAR技术利用激光进行探测和测距，不仅可以快速获取地面物体的三维坐标，还可以获得多个波段的地物光谱信息，可广泛用于地形测绘、土地覆盖分类、环境建模、森林资源调查等。本文提出了多光谱LiDAR的随机森林地物分类方法。该方法通过对LiDAR强度数据和高程数据提取分类特征，完成多光谱LiDAR的随机森林地物分类；并分析随机森林的特征贡献度特性，采用后向特征选择方法实现分类特征选择。通过对加拿大Optech Titan多光谱LiDAR数据的试验表明：随机森林方法可以获得较好的地物分类精度，而且可以适当地去除部分冗余和相关的特征，从而有效提高分类精度。

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

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.