| GF-6影像应用于林地与非林地识别的潜力分析 |
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| 引用本文： | 刘代超,李晓松,李向晨,杨广斌,杨珺婷.GF-6影像应用于林地与非林地识别的潜力分析[J].测绘通报,2020,0(8):5. |
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| 作者姓名： | 刘代超 李晓松 李向晨 杨广斌 杨珺婷 |
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| 作者单位： | 1. 贵州师范大学地理与环境科学学院, 贵州 贵阳 550000;2. 中国科学院遥感与数字地球研究所数字地球重点实验室, 北京 100094;3. 赤峰市林业科学研究院, 内蒙古 赤峰 024000 |
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| 基金项目： | 高分辨率对地观测系统重大专项应用共性关键技术（21-Y20A06-9001-17/18） |
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| 摘 要： | 为研究我国首颗携带红边波段的高分六影像（GF-6）在林地与非林地上的识别贡献，本文选择复杂林地类型的安徽省黄山市作为研究区，采用特征优选（RFE）与随机森林（RF）相结合的方法开展了林地与非林地识别潜力研究。首先根据实地调查、Google Earth影像及林地"一张图"样本数据构建了样本库；然后基于DEM、多时相光谱特征、植被指数、红边指数等特征开展分类，并比较不同模型精度及不同变量的重要度。结果表明：GF-6红边信息对林地非林地识别较为重要，引入红边信息可将总体分类精度提升2%，其他新增波段及地形特征对林地与非林地识别贡献并不明显；多时相数据的运用相比单时相数据可整体提高林地类型的分类精度2.93%~4.1%，单时相分类结果6月最好，9月次之，12月最差；特征优选可以有效减少数据输入维数（46到15），并取得最高分类精度，在不牺牲精度的同时保证了运算数据量的减少且明确了不同变量的贡献，具有较强的应用意义。
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| 关 键 词： | GF-6 林地与非林地 RFE 随机森林 特征重要性 |
| 收稿时间： | 2020-03-05 |
| 修稿时间： | 2020-06-16 |
Analysis of the potential of GF-6 WFV data for forest and non-forest land identification |
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| LIU Daichao,LI Xiaosong,LI Xiangchen,YANG Guangbin,YANG Junting.Analysis of the potential of GF-6 WFV data for forest and non-forest land identification[J].Bulletin of Surveying and Mapping,2020,0(8):5. |
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| Authors: | LIU Daichao LI Xiaosong LI Xiangchen YANG Guangbin YANG Junting |
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| Institution: | 1. School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550000, China;2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;3. Chifeng Research Institute of Forestry, Chifeng 024000, China |
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| Abstract: | In order to study the recognition contribution of the first GaoFen (GF-6) with red edge band in China on forest and non-forest land, this paper selectes Huangshan city, Anhui province, as a research area, and adopt feature optimization (RFE). The method combined with random forest (RF) has carried out research on the identification potential of forest and non-forest land. First it builds a sample database based on field surveys, Google Earth images, and forest land "one map" sample data. Then, it classifies based on characteristics such as DEM, multitemporal spectral features, vegetation index, red edge index, and compares the accuracy of different models and the importance of different variable. The results show that the red edge information of GF-6 is more important for the identification of forest and non-forest land. The introduction of red edge information can improve the overall classification accuracy by 2%. Compared with single-phase data, the use of data can improve the classification accuracy of forest types by 2.93%~4.1%. The single-phase classification results are the best in June, the second in September, and the worst in December. The preferred features can effectively reduce data input Dimensions (46 to 15), and achieve the highest classification accuracy. It does not sacrifice accuracy while ensuring a reduction in the amount of computing data and clarifying the contribution of different variables, which has strong application significance. |
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| Keywords: | GF-6 forest and non-forest land RFE random forest feature importance |
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