| 利用Sentinel-2A数据提取长江中下游丘陵地带农作物种植信息 |
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| 引用本文： | 陶莉,胡召玲.利用Sentinel-2A数据提取长江中下游丘陵地带农作物种植信息[J].测绘通报,2021,0(7):39-43. |
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| 作者姓名： | 陶莉 胡召玲 |
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| 作者单位： | 江苏师范大学地理测绘与城乡规划学院,江苏 徐州221116 |
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| 基金项目： | 国家自然科学基金（52074133）；江苏师范大学研究生科研实践创新计划（2020XKT033） |
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| 摘 要： | 长江中下游丘陵地带地块细小破碎、种植结构复杂，导致作物遥感光谱特征相互纠缠，信息精确提取困难等。本文基于Sentinel-2A数据提出了多特征组合优化的丘陵地带农作物种植结构精确识别方法。首先获取研究区内主要农作物的关键物候特征信息；然后计算其光谱特征、纹理特征、地形特征值，构建原始特征集；最后采用随机森林方法对特征进行重要性排序，对原始特征集进行特征变量优化，并选择优化后的组合特征进行监督分类提取出研究区农作物信息。试验结果表明，相较于单变量特征，通过多特征优化组合分类总体精度和Kappa系数分别从80.4%和0.748提高到96.3%和0.954，有效地提高了南方丘陵地带农作物分类精度，算法稳定性较强。在南方丘陵地带农作物的识别过程中，进行特征变量优化后的地形特征与纹理特征能显著提高分类精度。
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| 关 键 词： | 农作物 识别 Sentinel-2A 特征优化 特征提取 |
| 收稿时间： | 2021-03-02 |
| 修稿时间： | 2021-05-12 |
Crop planting structure identification based on Sentinel-2A data in hilly region of middle and lower reaches of Yangtze River |
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| TAO Li,HU Zhaoling.Crop planting structure identification based on Sentinel-2A data in hilly region of middle and lower reaches of Yangtze River[J].Bulletin of Surveying and Mapping,2021,0(7):39-43. |
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| Authors: | TAO Li HU Zhaoling |
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| Institution: | School of Geography, Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, China |
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| Abstract: | In order to solve the problems of small and broken parcels, complex planting structure, entanglement of crop remote sensing spectral features and difficulty in accurate information extraction in the hilly area of the middle and lower reaches of the Yangtze River, a more precise identification method of crop planting structure based on Sentinel-2A data is proposed in this paper. Firstly, the key phenological features of the main crops in the study area are obtained. Secondly, the spectral features, texture features and terrain features are calculated to construct the original feature sets. Finally, the importance of the features is sorted by using the random forest method, the feature variables of the original feature set are optimized, and the optimized combination features are selected for supervised classification to extract the crop information in the study area. The results show that:compared with the univariate feature, the overall classification accuracy and kappa coefficient are improved from 80.4% and 0.748 to 96.3% and 0.954 respectively which effectively improves the accuracy of crop classification in southern hilly area, and the algorithm robust is more stable. |
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| Keywords: | crops identification Sentinel-2A feature optimization feature extraction |
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