| 基于超高空间分辩率无人机影像的面向对象土地利用分类方法 |
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| 引用本文： | 刘舒,朱航.基于超高空间分辩率无人机影像的面向对象土地利用分类方法[J].农业工程学报,2020,36(2):87-94. |
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| 作者姓名： | 刘舒 朱航 |
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| 作者单位： | 1.吉林建筑大学测绘与勘查工程学院，长春 130118；,2.吉林大学机械与航空航天工程学院，长春 130022 |
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| 基金项目： | 国家重点研发计划项目（2016YFD0200701） |
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| 摘 要： | 为明确基于无人机超高空间分辨率影像的土地利用分类方法,尤其是有效特征和算法的选择,该研究获取吉林省德惠市一农耕区超高分无人机影像,获取区域正射影像图和数字表面模型,计算地形指标,采用面向对象方法进行土地利用分类研究。首先,采用随机森林算法,以光谱特征为基础,依次引入指数、形态、地形、纹理特征,建立5种特征选择方案,分析各类特征对分类效果的影响。其次,以Boruta特征选择算法获取的优化特征集为基础,采用随机森林算法、朴素贝叶斯算法、逻辑回归算法和支持向量机算法分类,分析不同算法的分类效果。结果表明:采用5种特征选择方案分类,引入形态特征时总体精度降低,引入其他特征时总体精度逐渐提高。5种特征共同参与的分类效果最佳,总体精度为98.04%,Kappa系数为0.980。错分主要发生在裸地和宅基地,漏分主要发生在草地、裸地、水渠和道路。错分和漏分主要是因为这几种类型对象具有相似的光谱、形态、纹理特征或相似的分布位置。采用优化特征集分类时,相比其他算法,随机森林算法更擅长处理高维特征集,获得最高的总体精度98.19%,最低的错分和漏分误差,分类效果最佳。借助无人机超高空间分辨率影像提取地形信息、形态信息,可以有效辅助土地利用分类,并能提高传统分类方法精度。
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| 关 键 词： | 遥感 土地利用 无人机影像 面向对象 特征选择 随机森林 |
| 收稿时间： | 2019/9/25 0:00:00 |
| 修稿时间： | 2020/1/7 0:00:00 |
Object-oriented land use classification based on ultra-high resolution images taken by unmanned aerial vehicle |
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| Liu Shu and Zhu Hang.Object-oriented land use classification based on ultra-high resolution images taken by unmanned aerial vehicle[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(2):87-94. |
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| Authors: | Liu Shu and Zhu Hang |
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| Institution: | 1.School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China; and 2.School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China |
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| Abstract: | Abstract: Unmanned aerial vehicle (UAV) has been increasingly used to aid agricultural production and land management, and this paper investigates the feasibility of using ultra-high resolution UAV images to differentiate land usage. We took a farmland at Dehui of Jilin province as an example and acquired its UAV images. The digital surface model (DSM) and the digital orthophoto map (DOM) of the region was generated using the digital photogrammetry software. We then calculated the regional terrain factors and the vegetation indices, and combined them with the original orthophoto model to construct the baseline images for land use classification. The tool of estimation of scale parameters (ESP) was used to extract the optimal scale for segmentation, and a level of objective was constructed after processing the multi-scale segmentation based on the optimal value of each parameter. Terrain and morphological features of each object was used for the classification. It included two steps. The first one was to perform an object-oriented classification of all the five features based on the random forest algorithm. We used a five-feature classifications to analyze the impact of different features. The first one only used the spectrum feature to classify and the index features, morphological features, terrain features and textural features were added consecutively for further classification. The overall accuracy, kappa coefficient, the omission errors and the commission errors for each feature were compared. In the second step, Boruta feature selection method was applied to the original feature space to obtain an optimal feature subset. Based on the optimal feature subset, land use classification was conducted using the random forest algorithm, naive Bayesian algorithm, logistic regression method and the support vector machine (SVM). Using the same optimal feature subset, the influence of each method on the classification was tested. The results showed that the accuracy of the five feature selection schemes was 93.72%, 97.35%, 96.93%, 97.77%, and 98.04% respectively. Adding morphological features reduced accuracy, while adding other features improved accuracy. The scheme with five features gave the best result. The commission was mainly between the bare land and residential land, and the omissions were mainly among grassland, water canals and roads. The confusion between them was likely to be caused by the similarity of spectral, morphology, textural properties and their similar positions. In this study, the important features for classification were spectrum features, textural features, index features and terrain features, and the least important features were morphological features. There were 72 features passing the Boruta test and forming the optimal feature subset. Based on this feature space, the overall accuracy with the above four different algorithms was 98.19%, 96.79%, 90.22% and 96.23% respectively. The classification using the random forest algorithm gave the best result. In conclusion, adding the terrain features can assist classification of land coverage and improve accuracy. Compared with other algorithms, the random forest algorithm is most robust in classification of land coverage in using high dimensional feature space. |
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| Keywords: | remote sensing land use type images taken by UAV object-oriented feature selection scheme random forest |
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