| 随机森林方法支持的复杂地形区土地利用/土地覆被分类研究 |
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| 引用本文： | 马慧娟,高小红,谷晓天.随机森林方法支持的复杂地形区土地利用/土地覆被分类研究[J].地球信息科学,2019,21(3):359-371. |
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| 作者姓名： | 马慧娟 高小红 谷晓天 |
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| 作者单位： | 青海师范大学地理科学学院 青海省自然地理与环境过程重点实验室,西宁 810008 |
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| 基金项目： | 青海省科技厅自然科学基金项目（2016-ZJ-907）;国家自然科学基金项目（41550003） |
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| 摘 要： | 随机森林方法目前已经成为遥感分类机器学习中一种有效方法,探索基于中等分辨率的Landsat卫星数据与随机森林方法相结合对复杂地形区长时间序列数据的获取及土地利用/土地覆被变化及模拟研究是非常有意义的。本文基于Landsat8OLI卫星多光谱数据,采用随机森林分类方法对青海省湟水流域复杂地形区土地利用类型进行了分类研究。针对复杂地形区域的情况,将研究区进行地理分区,根据每个分区的特点,选择相应的地形特征参数,并通过提取Landsat 8数据的光谱信息与纹理信息构建最优特征集,探索随机森林方法在复杂地形区土地利用分类的适用性。结果表明：使用Landsat8OLI数据进行随机森林分类,能较好地得到湟水流域复杂地形区域的土地利用类型结果;光谱、地形及纹理信息的结合在不同分区的表现结果不同。在脑山区光谱与地形信息结合能使随机森林分类效果最佳,总体精度达到91.33%,Kappa系数为0.886;而在浅山区与川水区综合考虑光谱、地形、纹理信息进行随机森林分类效果最佳,浅山区与川水区总体精度分别达到92.09%和87.85%,Kappa系数分别为0.902和0.859;利用随机森林算法进行优化选择纹理特征组合可以在保证分类精度的同时能够快速地提取土地利用类型信息,为复杂地形区土地利用类型的区分提供了实际可行的方法。
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| 关 键 词： | 随机森林算法 复杂地形区 土地利用/土地覆盖分类 特征选择 湟水流域 |
| 收稿时间： | 2018-07-27 |
Random Forest Classification of Landsat 8 Imagery for the Complex Terrain Area based on the Combination of Spectral,Topographic and Texture Information |
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| Huijuan MA,Xiaohong GAO,Xiaotian GU.Random Forest Classification of Landsat 8 Imagery for the Complex Terrain Area based on the Combination of Spectral,Topographic and Texture Information[J].Geo-information Science,2019,21(3):359-371. |
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| Authors: | Huijuan MA Xiaohong GAO Xiaotian GU |
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| Institution: | College of Geographical Sciences, Key Laboratory of Qinghai Province Physical Geography and Environmental Process Qinghai Normal University, Xining 810008, China |
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| Abstract: | Random forest classification has become an effective method in remote sensing classification of machine learning. It is of great significance to combine the Landsat satellite data and random forest method to obtain long time series data in the complex terrain areas and to explore its land use/land cover change. Based on the multi-spectral data of landsat8 OLI satellite, this paper adopted the random forest classification method to classify the land use types of Huangshui basin complex topography areas in Qinghai province. According to the characteristics of complex terrain areas, the study area was divided into different geographical regions. The topographic parameters were then selected, and the optimal feature collection was constructed by extracting spectral and texture information of Landsat8 data. The objective of this papers was to explore the applicability of random forest methods in land use classification on the complex topographic regions. The results showed that RFC classification with the landsat8 OLI data can be well used to obtain the land use types in the Huangshui basin. The combination of spectral, topographic, and texture information performed differently in different areas. In the middle and high mountain areas, the combination of spectral and topographic information can obtain the best results in the random forest classification with the overall accuracy of 91.33% and Kappa coefficient of 0.886. In the shallow mountain areas and valley plain, however, the random forest classification can obtain the best results by combining spectral, topographic, and texture information with the overall accuracy of 92.09% and 87.85% and Kappa coefficient of 0.902 and 0.859, respectively. Using the random forest algorithm to optimize the selection of texture feature combination can extract the land use type information quickly and ensure its accuracy. Random forest classification combined multi-source information can be used effectively to classify land use types, which can provide some enlightenment and reference values for the renewal of land use status and the development of social economy in the study area. |
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| Keywords: | random forest algorithm complex terrain area land use/land cover classification feature selection the Huangshui basin |
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