基于主成分分析的农作物空间分布信息提取

Extraction of Crop Spatial Distribution Information Based on Principal Component Analysis

针对遥感农作物分类精度低、作物区分不明显的特点,本文提出了一种基于主成分分析的农作物空间分布信息提取方法。通过主成分分析,增强影像的光谱特征,提高样本的可分离性和影像分类精度,满足农作空间分布识别要求。最后以GF-1卫星影像为研究对象进行试验,结果表明,本文提出的方法分类精度可达95%以上,实验结果符合实际情况。

In view of the low precision of remote sensing crop classification and the indistinct distinction between crops,this paper proposes a kind of crop spatial distribution information extraction based on principal component analysis(PCA).Through PCA,the spectral characteristics of the image are enhanced,the separability of the samples is improved,the accuracy of image classification is improved,and the recognition requirements of agricultural spatial distribution are met.In this paper,GF-1 satellite image is taken as the research object to carry out method experiments.The experimental results show that the classification accuracy of the method proposed in this paper can reach more than 95%,and the experimental results are in line with the actual situation.