【读论文】An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data（2013）

周伟奇
基于对象的城市土地覆盖分类方法:融合LiDAR高度和强度数据

摘要：
Abstract—Digital surface models (DSMs) derived from light detection and ranging (LiDAR) data have been increasingly integrated with high-resolution multispectral satellite/aerial imagery for urban land cover classification. Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of urban land cover classification, particularly in highly developed urban settings.
基于激光探测和测距(LiDAR)数据的数字地表模型(DSM)已越来越多地与高分辨率多光谱卫星、航空影像相结合，用于城市土地覆盖分类。然而，很少有研究调查LiDAR强度值在城市土地覆盖分类中的作用，特别是在高度发达的城市环境中。

In this letter, we use an object-based classification approach to investigate whether a combination of LiDAR height and intensity data can accurately map urban land cover. We further compare the approach to a method that uses multispectral imagery as the primary data source, but LiDAR DSM as ancillary data to aid in classification.
本文中，我们使用基于对象的分类方法来研究融合LiDAR高度和强度信息是否可以准确地绘制城市土地覆盖情况。我们进一步将该方法与使用多光谱图像作为主要数据源的方法进行了比较，LiDAR DSM作为辅助数据来帮助分类的方法。

The study site is a suburban area in Baltimore County, MD. The LiDAR data were acquired in March 2005, from which DSM and two intensity layers (first and last returns), with 1-m spatial resolution were generated, respectively. Four classes were included: 1) buildings; 2) pavement; 3) trees and shrubs; and 4) grass.
研究地点位于马里兰州巴尔的摩县郊区。LiDAR数据于2005年3月获得，从中分别生成了1 m空间分辨率的DSM和两个强度值(首次和末次回波)。其中包括四类:1)建筑;2)路面;3)乔灌木;和4)草。

Our results indicated that the objectbased approach provided flexible and effective means to integrate LiDAR height and intensity data for urban land cover classification. A combination of the LiDAR height and intensity data proved to be effective for urban land cover classification.
结果表明，基于对象的融合LiDAR高度和强度方法为城市土地覆盖分类提供了灵活有效的手段。结合LiDAR的高度和强度数据进行城市土地覆盖分类是有效的。

The overall accuracy of the classification was 90.7%, and the overall Kappa statistics equaled 0.872, with the user’s and producer’s accuracies ranging from 86.8% to 93.6%. The accuracy of the results were far better than those using multispectral imagery alone, and comparable to using DSM data in combination with high-resolution multispectral satellite/aerial imagery.
分类总体准确率为90.7%，总的Kappa统计值为0.872，用户和生产者的准确率在86.8% ~ 93.6%之间。结果的准确性远远优于单独使用多光谱图像的结果，与结合使用DSM数据和高分辨率过光谱卫星、航空影像的结果相当。

关键词：
Index Terms—Baltimore, high-resolution imagery, intensity, light detection and ranging (LiDAR), normalized digital surface model (nDSM), object-based image analysis, urban land cover classification.
关键词：巴尔的摩、高分辨率图像、强度、光探测和测距(LiDAR)，标准化数字表面模型(nDSM)、基于对象的图像分析、城市土地覆盖分类

结论：
This research investigated whether the use of LiDAR data alone can be effectively map detailed urban land cover, using an object-based classification approach. Our results indicated that using an object-based classification approach, a combination of the LiDAR height and intensity data could accurately characterize and map urban land cover.
本研究采用基于对象的分类方法，研究仅使用LiDAR数据是否可以有效地绘制详细的城市土地覆盖。结果表明，采用基于对象的分类方法，融合LiDAR的高度和强度数据可以准确地描述和绘制城市土地覆盖情况。

The accuracy of the results was far better than those using multispectral imagery alone, and comparable to those integrating LiDAR data with multispectral imagery and existing GIS layers.
The objectbased approach provided a flexible and effective means of integrating LiDAR height and intensity information for urban land cover classification, and was superior to a pixel-based approach.
结果表明，与单纯使用多光谱图像相比，激光LiDAR数据与多光谱图像和现有GIS层相结合的结果具有相当的准确性。
基于对象的方法为城市土地覆盖分类提供了一种灵活有效的融合激光LiDAR高度和强度信息的方法，并且优于基于像素的方法。

As LiDAR nDSM are relatively consistent and stable across a heterogeneous urban landscape, and thus allows for automatic feature extraction for a large region, using an object-based approach, the integration of LiDAR nDSM and intensity provides great potential for accurate large-scale mapping of detailed urban land cover.
由于激光LiDAR nDSM在不同类型的城市景观中具有相对的一致性和稳定性，因此可以实现大区域特征的自动提取。因此，采用基于对象的方法，激光LiDAR nDSM和强度的集成为精确绘制城市详细覆盖的大尺度地形图提供了巨大的潜力（新的研究思路）。

1.该论文研究了什么？
Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of urban land cover classification, particularly in highly developed urban settings.
在高度发达的城市环境中，激光liDAR强度值在城市土地覆盖分类中作用

In addition to height data, LiDAR also provides intensity data that reflect the material characteristics of land cover features, which can be potentially used for urban land cover classification [6], [7]. While LiDAR intensity data have been increasingly used in forest-type classification [8], only a few very recent studies have used LiDAR intensity as ancillary data to aid in urban land cover mapping [4], [6]. Few studies have investigated the usefulness of LiDAR data alone, i.e., a combination of LiDAR height and intensity information, in urban land cover classification [7], [9], particularly in highly developed urban settings, where classification is more challenging due to the fine-scale complexity of urban land cover features. This letter aims to fill this gap.
除了高度数据外，激光雷达还提供反映土地覆盖特征物质特征的强度数据，这些数据可能用于城市土地覆盖分类[6]、[7]。虽然激光雷达强度数据越来越多地用于森林类型分类[8]，但只有少数最近的研究使用激光雷达强度作为辅助数据来帮助绘制城市土地覆盖图[4]，[6]。在城市土地覆盖分类[7]，[9]中，特别是在高度发达的城市环境中，由于城市土地覆盖特征的细尺度复杂性，分类更具挑战性，很少有研究单独调查激光雷达数据(即激光雷达高度和强度信息的组合)的有用性。这篇论文旨在填补这一空白。

2.创新点在哪？
Paralleled with the increasing availability of LiDAR data are the advances in object-based image analysis (OBIA), an image classification approach that has gained wide acceptance in fine-scale urban land cover mapping [10]. Rather than classifying individual pixels, object-based classification segments the imagery into objects. Consequently, in addition to spectral response, object characteristics, such as shape and spatial relations, can be used for classification [1], [10]. Many studies have shown that OBIA techniques are superior to pixel-based approaches for land cover classification from high-resolution imagery [10].
与LiDAR数据日益丰富的同时，基于对象的图像分析(OBIA)技术也取得了进展，这是一种在城市土地覆盖精细制图中得到广泛接受的图像分类方法[10]。基于对象的分类不是对单个像素进行分类，而是将图像分割成对象。因此，除了光谱响应之外，物体特征(例如形状和空间关系)可用于分类[1]、[10]。许多研究表明，在从高分辨率图像中进行土地覆盖分类时，OBIA技术优于基于像素的方法[10]。

3.研究方法是什么？
In this letter, we used an object-based classification approach to investigate whether a combination of the LiDAR height and intensity data can accurately characterize and map urban land cover. We further compared this approach to a method that used imagery as the primary data source, but LiDAR height data as ancillary data for classification.
在这封信中，我们使用基于对象的分类方法来研究激光雷达高度和强度数据的组合是否可以准确地描述和绘制城市土地覆盖。我们进一步比较了这种方法与一种以图像作为主要数据源，而激光雷达高度数据作为辅助数据进行分类的方法。

4.得到的结论是什么？
This research investigated whether the use of LiDAR data alone can be effectively map detailed urban land cover, using an object-based classification approach. Our results indicated that using an object-based classification approach, a combination of the LiDAR height and intensity data could accurately characterize and map urban land cover.
本研究采用基于对象的分类方法，研究仅使用LiDAR数据是否可以有效地绘制详细的城市土地覆盖。结果表明，采用基于对象的分类方法，融合LiDAR的高度和强度数据可以准确地描述和绘制城市土地覆盖情况。