基于无人机获取的甚高分辨率可见光影像的城市化区域识别
Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery
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中文总结 AI 辅助
本研究比较了无人机RGB影像中植被指数阈值分割与神经网络在城市化区域识别上的性能，发现神经网络精度更高（约96%），并分析了季节与图像块大小的影响。
中文摘要 AI 辅助
本研究比较了基于无人机（UAV）获取的RGB影像区分城市化区域与非城市化区域的分类器。测试的解决方案包括多种植被指数（VIs）阈值分割和神经网络（NNs）。分析针对两个研究区域进行，这些区域的调查使用了不同的无人机和相机。两个研究区域的地面采样距离分别为10毫米和15毫米。参考分类为人工进行，第一个区域获得了约2400万个分类像素，第二个区域约380万个。本研究包括分析季节对测试VIs阈值的影响，以及作为NNs输入的图像块大小对分类精度的影响。研究结果表明，使用NNs的分类精度（约96%）高于测试的最佳VI，即Excess Blue（约87%）。由于所用数据集的高度不平衡性（非城市化区域约占整个数据集的87%），还使用了马修斯相关系数来评估分类的正确性。基于统计度量的分析辅以分类结果的定性评估，这有助于识别VIs阈值分割与NNs之间分类差异的最重要来源。
英文摘要
This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery. The tested solutions in-cluded numerous vegetation indices (VIs) thresholding and neural networks (NNs). The analysis was conducted for two study areas for which surveys were carried out using different UAVs and cameras. The ground sampling distances for the study areas were 10 mm and 15 mm, respectively. Reference classification was performed manually, obtaining approximately 24 million classified pix-els for the first area and approximately 3.8 million for the second. This research study included an analysis of the impact of the season on the threshold values for the tested VIs and the impact of image patch size provided as inputs for the NNs on classification accuracy. The results of the con-ducted research study indicate a higher classification accuracy using NNs (about 96%) compared with the best of the tested VIs, i.e., Excess Blue (about 87%). Due to the highly imbalanced nature of the used datasets (non-urbanized areas constitute approximately 87% of the total datasets), the Mat-thews correlation coefficient was also used to assess the correctness of the classification. The analysis based on statistical measures was supplemented with a qualitative assessment of the classification results, which allowed the identification of the most important sources of differences in classification between VIs thresholding and NNs.
发表机构
- AGH University of Krakow（克拉科夫AGH大学）
- The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark（南丹麦大学马士基·麦金尼·穆勒研究所）
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