UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis
UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis
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DOI：
10.3390/rs70101074
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发表时间：
2015-01-01
期刊：
影响因子：
5
通讯作者：
Gong, Jianhua
中科院分区：
文献类型：
--
作者：
Feng, Quanlong;Liu, Jiantao;Gong, Jianhua
无人机遥感由于在低空获取的超高分辨率图像，在复杂城市景观的植被制图中具有很大的潜力。由于载荷能力的限制，现成的数码相机被广泛应用于中小型无人机上。通过结合纹理特征和鲁棒分类器，可以降低数字相机在植被制图中光谱分辨率低的局限性。随机森林在卫星遥感应用中得到了广泛的应用，但在无人机图像分类中的应用还没有很好的文献记录。本文的目的是提出一种基于随机森林和纹理分析的混合方法来准确区分城市植被区域的土地覆盖，并分析分类精度随纹理窗口大小的变化规律。在9种不同窗口大小下计算6个最小相关二阶纹理度量，并将其添加到原始红绿蓝（RGB）图像中作为辅助数据。采用由200棵决策树组成的随机森林分类器对光谱-纹理特征空间进行分类。结果表明：(1)随机森林在城市植被分类中优于传统的极大似然分类器，表现出与基于目标的图像分析相似的分类效果；(2)纹理特征的加入显著提高了分类准确率；(3)分类精度与纹理窗口大小呈倒U型关系。结果表明，无人机为城市植被制图提供了一个高效、理想的平台。本文提出的混合方法在城市植被制图中具有良好的判别效果。同时采用随机森林和纹理分析可以减少现有数码相机的缺点。
Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB) images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1) Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2) the inclusion of texture features improved classification accuracy significantly; (3) classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time.