Object-Based Land Cover Classification Using High-Posting-Density LiDAR Data

This study introduces a method for object-based land cover classification based solely on the analysis of LiDAR-derived information—i.e., without the use of conventional optical imagery such as aerial photography or multispectral imagery. The method focuses on the relative information content from height, intensity, and shape of features found in the scene. Eight object-based metrics were used to classify the terrain into land cover information: mean height, standard deviation (STDEV) of height, height homogeneity, height contrast, height entropy, height correlation, mean intensity, and compactness. Using machine-learning decision trees, these metrics yielded land cover classification accuracies > 90%. A sensitivity analysis found that mean intensity was the key metric for differentiating between the grass and road/parking lot classes. Mean height was also a contributing discriminator for distinguishing features with different height information, such as between the building and grass classes. The shapeor texture-based metrics did not significantly improve the land cover classifications. The most important three metrics (i.e., mean height, STDEV height, and mean intensity) were sufficient to achieve classification accuracies > 90%. INTRODUCTION LiDAR (Light Detection And Ranging) is an active optical remote sensing system that generally uses near-infrared laser light to measure the range from the sensor to a target on the Earth surface (Jensen, 2007). LiDAR range measurements can be used to identify the elevation of the target as well as its precise planimetric location. These two measurements are the fundamental building blocks for generating digital elevation models (DEMs). Digital elevation information is a critical component in most geographic information system (GIS) databases used by many agencies such as the United States Geological Survey (USGS) and Federal Emergency Management 1Corresponding author; email: [email protected] 209 GIScience & Remote Sensing, 2008, 45, No. 2, p. 209–228. DOI: 10.2747/1548-1603.45.2.209 Copyright © 2008 by Bellwether Publishing, Ltd. All rights reserved. 210 IM ET AL. Administration (FEMA). DEMs can be subdivided into: (1) digital surface models (DSMs) containing height information of the top “surface” of all features in the landscape, including vegetation and buildings,; and (2) digital terrain models (DTMs) containing elevation information solely about the bare Earth (i.e., no above-surface heights) surface (Jensen, 2007). Most LiDAR remote sensing systems that are used for terrestrial topographic mapping use near-infrared laser light from 1040 to 1060 nm, whereas blue-green laser light (at approximately 532 nm) is used for bathymetric mapping due to its water penetration capability (Mikhail et al., 2001; Boland et al., 2004). Because each LiDAR point is already georeferenced, LiDAR data can be directly used in spatial analysis without additional geometric correction (Flood and Gutelius, 1997; Jensen and Im, 2007). Most LiDAR remote sensing systems now provide intensity information as well as multiple returns representing surface heights. The return with the maximum energy of all returned signals for a single pulse is usually recorded as the intensity for that pulse (Baltsavias, 1999). Other factors such as gain setting, bidirectional effects, the angle of incidence, and atmospheric dispersion also influence the intensity values recorded. Leonard (2005) points out that systems with automatic gain control adjust the return signal gain in response to changes in target reflectance. Thus, such variability in intensity values can make it problematic to interpret or model the intensity data. LiDAR data have been recently used in a variety of applications including topographic mapping, forest and vegetation, and land cover classification. Some studies have demonstrated the effectiveness of LiDAR for DEM-related terrain mapping (Hodgson et al., 2005; Toyra and Pietroniro, 2005; Raber et al., 2007). Many researchers have adopted LiDAR remote sensing for identifying vegetation structure and tree species (Hill and Thomson, 2005; Suarez et al., 2005; Bork and Su, 2007; Brandtberg, 2007). LiDAR data have also been used for land cover classification (Song et al., 2002; Hodgson et al., 2003; Lee and Shan, 2003; Rottensteiner et al., 2005). Most previous research focused on integrating LiDAR data with other GIS-based ancillary data and/or remote sensing data such as multispectral imagery. LiDAR data have been generally used as ancillary data to improve land cover classification accuracy. Data fusion between LiDAR and other remote sensing data was a critical part of the studies. Some studies fused the data at a pixel level, whereas others performed data fusion at a decision level. LiDAR data obtained at very high posting densities (i.e., < 0.5 m) increases its potential in a variety of applications. High-posting-density LiDAR-derived information may be used to identify not only elevation of a landform but also precise height and shape information of a target on the Earth’s surface such as vehicle or tree. The question then becomes, “Can such high-posting-density LiDAR data be used as the sole information source in applications such as image classification— without other ancillary data?” This study explores the applicability of high-posting-density LiDAR data for land cover classifications with an object-based approach focusing on height information. For example, different land cover classes such as buildings and trees may have similar heights yet different shapes. Various height-related metrics such as mean, standard deviation, and textures were extracted for objects as well as a shape index, such as compactness. LiDAR-derived intensity data were also incorporated in the analysis to determine its relative usefulness in land cover classification. In OBJECT-BASED LAND COVER CLASSIFICATION 211 summary, the objectives of this study are to: (1) explore the applicability of highposting-density LiDAR-derived features for land cover classification without incorporating other remote sensing data, such as multispectral imagery; (2) evaluate the capability of object-based metrics with machine-learning decision trees to classify the LiDAR data; and (3) assess the sensitivity of each object-based metric for land cover classification.