红外, 2019, 40 (3): 24, 网络出版: 2019-04-11  

基于决策树和神经网络的农作物分类研究——以廊坊市为例

Crop Classification Research Based on Decision Tree and Neural Network——Take Langfang City as an Example
李龙 1,2,*李旭青 1,2吴伶 3杨秀峰 1,2孙鹏飞 1,2
作者单位
1 北华航天工业学院,河北 廊坊 065000
2 河北省航天遥感信息处理与应用协同创新中心,河北 廊坊 065000
3 中国地质大学(北京),北京 100083
摘要
以河北省廊坊市永清县整个县域为研究区,以GF1-WFV 16 m分辨率影像为数据源,选取覆盖作物完整生长期多个时相的影像数据,构建作物归一化植被指数(Normalized Difference Vegetation Index,NDVI)时间序列。通过对研究区NDVI曲线的分析,发现利用该数据构建的NDVI时间序列可描述研究区作物的生长特性,体现当地不同作物的物候差异,能有效地区分出当地的种植模式。选取NDVI曲线上最大值、最小值、峰值的出现时间、峰值数量和阈值等特征参数构建决策树。根据研究区的物候历和对当地种植结构的调查,利用最佳时相的影像,针对某一种或特定几种作物进行分类提取。分别采用决策树分类、神经网络分类等方法进行精度验证,综合比较得出最佳的作物分类方法。研究结果表明,在永清县这一县域研究区,利用GF1-WFV 16 m分辨率多时相遥感数据进行作物分类,采用决策树分类、神经网络分类两种方法的精度分别为72.0729%、87.3%。利用决策树分类的效果最优。
Abstract
Taking the entire county of Yongqing County, Langfang City, Hebei Province as the research area, the GF1-WFV 16-meter resolution image was used as the data source, and the image data covering multiple phases of the crop growth period was selected to construct the nonralized difference vegetation index (NDVI) time series of the crop. By analyzing the NDVI curve of the study area, it was found that the NDVI time series constructed with the data could describe the growth characteristics of crops in the study area, reflect the phenological differences of different crops in the region, and effectively distinguish the local planting patterns. The decision tree is constructed by selecting characteristic parameters such as the occurrence time of the maximum value, minimum value, peak, peak number and threshold value on the NDVI curve. According to the phenological calendar of the study area and the investigation of the local planting structure, the optimal time phase image was used to classify and extract one or several crops. Decision tree classification and neural network were used respectively, and accuracy verification was carried out to obtain the best crop classification method by comprehensive comparison. The results show that in the county research area of Yongqing County, when the GF1-WFV 16-meter resolution multi-temporal remote sensing data is used for crop classification, the accuracy of decision tree classification and neural network classification are 72.0729% and 87.3%, respectively. The optimal classification is obtained by using decision tree.
参考文献

[1] 戴建国,张国顺,郭鹏,等.基于无人机遥感可见光影像的北疆主要农作物分类方法 [J].农业工程学报,2018,34(18):122-129.

[2] 陈仲新,任建强,唐华俊,等.农业遥感研究应用进展与展望 [J].遥感学报,2016,20(5):748-767.

[3] 李旭青,刘湘南,刘美玲,等.水稻冠层氮素含量光谱反演的随机森林算法及区域应用 [J]. 遥感学报,2014,18(4):934-945.

[4] 刘佳,王利民,杨福刚,等.基于HJ时间序列数据的农作物种植面积估算 [J].农业工程学报,2015,31(3):199-206

[5] 张健康,程彦培,张发旺,等.基于多时相遥感影像的作物种植信息提取 [J]. 农业工程学报,2012,28(2):134-141.

[6] 杨闫君,占玉林,田庆久,等.基于GF-1/WFV NDVI时间序列数据的作物分类 [J].农业工程学报,2015,31(24):155-161.

[7] 常布辉,王军涛,罗玉丽,等.河套灌区沈乌灌域GF-1/WFV遥感耕地提取 [J].农业工程学报,2017,33(23):188-195.

[8] 姜晓剑,刘小军,田永超,等.基于遥感影像的作物生长监测系统的设计与实现 [J].农业工程学报,2010,26(3):156-162.

[9] 马丽,徐新刚,贾建华,等.利用多时相TM影像 进行作物分类方法 [J].农业工程学报,2008,24(S2):191-195.

[10] 申文明,王文杰,罗海江,等.基于决策树分类技术的遥感影像分类方法研究 [J].遥感技术与应用,2007,22(3):333-338.

[11] 胥海威.基于改进随机聚类决策森林算法的遥感影像分类研究 [D].长沙:中南大学,2012.

[12] 张荣群,王盛安,高万林,等.基于时序植被指数的县域作物遥感分类方法研究 [J].农业机械学报,2015,46(S1):246-252.

[13] Liu J,Tian Q J,Huang Y,et al.Extraction of the Corn Planting Area Based on Multi-temporal HJ-1 Satellite Data [C]. Shanghai: the 19th International Conference on Geoinformatics, 2011.

李龙, 李旭青, 吴伶, 杨秀峰, 孙鹏飞. 基于决策树和神经网络的农作物分类研究——以廊坊市为例[J]. 红外, 2019, 40(3): 24. LI Long, LI Xu-qing, WU Ling, YANG Xiu-feng, SUN Peng-fei. Crop Classification Research Based on Decision Tree and Neural Network——Take Langfang City as an Example[J]. INFRARED, 2019, 40(3): 24.

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!