Improved Classification Accuracy Based on the Output-Level Fusion of High-Resolution Satellite Images and Airborne LiDAR Data in Urban Area
Improved Classification Accuracy Based on the Output-Level Fusion of High-Resolution Satellite Images and Airborne LiDAR Data in Urban Area
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DOI：
10.1109/lgrs.2013.2273397
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发表时间：
2014-03-01
影响因子：
4.8
通讯作者：
Kim, Yongil
中科院分区：
文献类型：
--
作者：
Kim, Yongmin;Kim, Yongil
本文提出了一种基于高分辨率卫星图像和机载光探测和测距（LiDAR）数据的融合方法，以提高分类精度。基于输出级融合的分类过程中，所提出的方法利用一个三步的过程，以尽量减少错误分类的建筑物和道路对象。首先，高架道路区域检测地面点，这是提取的数字地形模型的基础上产生的统计值。其次，通过各种数据结果的输出级融合，从卫星图像中提取建筑物信息。第三，使用支持向量机对缺乏高架道路和建筑物的区域进行监督分类。我们通过将其与基于像素的方法进行比较并分析实验WorldView-2图像和机载LiDAR数据来评估所提出的方法。我们进行了目视判读和定量准确性评估。该方法的总体准确率和kappa系数分别为90.91%和0.892。这些结果表明，与基于像素的方法相比，整体准确性和Kappa系数分别提高了11.27个百分点和0.135。结果证实，我们提出的方法具有显着的潜力，使用高分辨率卫星图像和机载激光雷达数据进行城市环境分类。
This letter proposes a method based on the fusion of high-resolution satellite images and airborne light detection and ranging (LiDAR) data for improving classification accuracy. Based on output-level fusion during classification, the proposed method utilizes a three-step process to minimize the misclassification of buildings and road objects. First, elevated road areas are detected in ground points, which are extracted for the generation of a digital terrain model based on statistical values. Second, building information is extracted from a satellite image through the output-level fusion of various data results. Third, supervised classification is conducted using a support vector machine for areas that lack elevated roads and buildings. We evaluated the proposed method by comparing it with a pixel-based method and analyzing experimental WorldView-2 images and airborne LiDAR data. We conducted a visual interpretation and quantitative accuracy assessment. The overall accuracy and kappa coefficient of the proposed method were 90.91% and 0.892, respectively. These results demonstrated an improvement in the overall accuracy and kappa coefficient by 11.27 percentage points and 0.135, respectively, compared with the pixel-based method. The results confirmed that our proposed method has significant potential for classifying urban environments using high-resolution satellite imagery and airborne LiDAR data.