A Geographic Object-Based Approach for Land Classification Using LiDAR Elevation and Intensity

Abstract

By providing detailed height and intensity land surface information, high-resolution LiDAR data have proved to be effective in supporting land classification when combined with other major geospatial data sources, such as hyperspectral images. However, rectifying and fusing multisource geospatial data involves what normally is a manual and time-consuming process. In this letter, we propose a geographic object-based image analysis approach to enable semiautomatic land classification and mapping using LiDAR elevation and intensity data. The methodological framework consists of a series of operations, including preprocessing, object-based segmentation, creation of statistical variables from elevation and intensity, and semisupervised classification. We have successfully applied this approach to the classification of multiple land features, including asphalt, grass, barren land, swimming pool, shrubland, pavement, and buildings. Results show that our proposed approach performs better than LiDAR analysis methods in classifying different land parcels.