基于SVM的县域冬小麦种植面积遥感提取

Remote Sensing Extraction of Winter Wheat Planting Area Based on SVM

冬小麦种植面积的精确提取,对于农业部门进行冬小麦生长监测与产量估测有着重要的支撑作用。本研究在对Landsat-8卫星15 m×15 m空间分辨率遥感影像进行预处理的基础上,基于最佳波段指数(OIF),采用支持向量机(SVM)算法中四种核函数进行影像分类,并比较分类精度,选择精度最高的核函数作为SVM最优核函数对盐城市大丰区冬小麦种植面积进行提取,与最大似然法、最小距离法的结果进行对比。结果表明,四种核函数中,Linear核函数分类精度最高,达到98.56%。将Linear核函数作为SVM最优核函数对大丰区冬小麦种植面积进行提取,提取到的种植面积为71 834.4 hm^2,提取精度、分类精度和Kappa系数分别为91.25%、98.56%和0.98。基于SVM的冬小麦面积提取效果明显好于传统监督分类方法,说明使用支持向量机与影像光谱特征进行影像分类能够准确提取县域冬小麦种植面积。

The accurate extraction of winter wheat area plays an important role in the winter wheat growth monitoring and yield estimation in the agricultural sector. In this study, the 15 m×15 m spatial resolution remote sensing image from Landsat-8 satellite was preprocessed. Then, based on the optimal band index(OIF), four kernel functions in support vector machine(SVM) algorithm were used in image classification.Their classification accuracies were compared. The highest precision kernel function was selected as the optimal kernel function of SVM to extract the winter wheat planting area in Dafeng District, and compared with the results of maximum likelihood method and minimum distance method. The results showed that among the four kernel functions, the Linear kernel function had the highest classification accuracy. And it was 98.56%. The winter wheat planting area extracted in Dafeng District with the Linear kernel function as the SVM optimal kernel function was 71 834.4 hm^2. The extraction precision accuracy, classification accuracy and Kappa coefficient were 91.25%, 98.56% and 0.98, respectively. The extraction effect of Winter wheat area based on SVM was significantly better than the traditional supervised classification method. It was suggested that the image classification based on support vector machine and image spectral features could accurately extract winter wheat planting area. The method could provide method support for the extraction of winter wheat area in the counties of Jianghuai region.