应用GF-5高光谱遥感影像提取山区茶园

Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing Imagery in the Mountainous Area

为探究高光谱影像丰富的光谱信息对山区茶园的提取效果,促进国产卫星高光谱影像在茶园分布制图与资源监测中的应用,以普洱市南部山区茶园典型分布区为研究区,以高分五号AHSI(GF-5 AHSI)影像以及数字高程模型(DEM)为主要数据源,结合实地调查数据,构建基于随机森林(RF)分类器的亚热带山区茶园提取算法。首先,将去除噪声影响后剩余的250个波段作为光谱特征(SF);在分析研究区主要地物(茶园、森林、农田)光谱的基础上,分别构建植被指数特征(VIF)45个,地形特征(TRF)3个。然后,利用RF进行特征重要性排序,按照特征重要性从大到小依次将特征输入RF分类器进行茶园提取,随着特征的不断输入,茶园提取的F1-Score达到饱和不再明显增加时的特征维度即为最优选择特征。依据3种特征因子(SF,VIF和TRF)构建12种分类方案。最后,比较12种方案的茶园提取精度,最终确定最优方案。结果显示,不同特征组合茶园提取F1-Score排序为SF+VIF+TRE+FS>TRF+VIF+FS>SF+TRF+FS>TRF+VIF>VIF+FS>SF+VIF+FS>SF+VIF+TRF>SF+FS>SF+TRF>VIF>SF+VIF>SF。FS后6种分类方案中,SF参与分类的4种方案中被选中2次的波段为b4、b5、b6、b27、b133、b150和b281;在VIF参与分类的4种方案中被选中4次的指数分别为REP、VOG2、SR2、SR3、WBI、TIP3和TIP9;TRF参与分类的4种方案中坡度、坡向、高程均被选中。此外,SF+TRF+VIF特征进行FS后结合RF算法能够对亚热带茶园分布进行有效识别,具有较好的识别精度和可信度,GF-5 AHSI卫星数据在茶园分布制图和资源监测等领域有着较好的应用潜力和前景。

To explore the extraction effect of the rich spectral information of hyperspectral images on tea plantations in mountainous regions and to promote the application of domestic satellite hyperspectral images in tea plantations distribution mapping and resource monitoring.Taking the typical distribution area of tea plantations in the southern mountainous region of Pu er City as the study area,an algorithm for tea plantations extraction in subtropical mountainous regions based on the random forest(RF)classifier was constructed,and the main data sources,including the hyperspectral 5 AHSI(GF-5 AHSI)image,digital elevation model(DEM),and the field survey data.Firstly,the 250 bands of GF-5 AHSI image after removing the noise bandsused as spectral features(SF).Based on the spectral analysis of the main features(tea plantation,forest and cropland)in the study area and DEM data,45 vegetation index features(VIF)and 3 topographic features(TRF)were constructed,respectively.Moreover,the RF was used to rank the feature importance of each feature,and the features were input into the RF classifier for tea plantations extraction in order of feature importance from highest to lowest.The feature dimension of the optimal feature space is determined when the F1-Score of the tea plantations reaches saturation and no longer increases significantly with the continuous input of features.Finally,12 classification schemes were constructed based on 3 feature factors(SF,VIF and TRF).Moreover,the accuracy of tea plantations extraction was compared among the 12 schemes and the optimal scheme was finally determined.The results showed that the producer s accuracy(PA)and user s accuracy(UA)of the 6 classification schemes after feature selection(FS)were better than those of the 6 schemes before FS.The VIF+TRF scheme had the best extraction accuracy(PA:89.72%,UA:81.97%)among the 6 classification schemes before FS,while the best performance of tea plantations extraction accuracy after FS was the SF+VIF+TRE scheme(PA:90.69%,UA:83.09%).The F1-Score of tea plantations extraction with different feature combinations was ranked as SF+VIF+TRE+FS>TRF+VIF+FS>SF+TRF+FS>TRF+VIF>VIF+FS>SF+VIF+FS>SF+VIF+FS>SF+VIF+TRF>SF+FS>SF+TRF>VIF>SF+VIF>SF.Among the 6 classification schemes after FS,the bands that were selected twice in the 4 schemes in which SF was involved in classification were b4,b5,b6,b27,b133,b150 and b281;the indices that were selected four times in the 4 schemes in which VIF was involved in classification were REP,VOG2,SR2,SR3,WBI,TIP3 and TIP9 and all terrain factors were selected in the 4 classification schemes in which TRF participated.SF+TRF+VIF features combined with the RF algorithm after FS can effectively identify the distribution of subtropical tea plantations with good recognition accuracy and credibility.The GF-5 AHSI satellite data has good potential and prospects for application in tea plantations distribution mapping and resource monitoring.