基于网格法分集和主动学习的高光谱图像分类方法

A Hyperspectral Image Classification Method Based on Grid Diversity and Active Learning

针对高光谱图像分类过程中分类精度低和样本数量较少的问题,文中提出了一种基于网格法分集和主动学习的图像分类方法。该方法利用网格法将主成分空间划分成若干网格,在每个含有样本的网格中随机挑选一个样本,并将其原始光谱数据归入训练集;随后,采用主动学习方法,在其余样本中用K-近邻法选择不确定性最大的若干样本并入训练集,从而扩充了训练集,并使数据集具有代表性,提升了分类精度。同时,在数据处理过程中,联合运用主成分分析和线性判别分析对光谱数据进行降维,进一步提高了运算速度。实验结果表明,在Indian Pines高光谱数据集中,在少量训练集样本的情况下,该方法相较于随机分集和非主动学习,分别将总体分类精度提升了12.24%和19.76%。

In order to solve the problems of low classification accuracy and small number of samples in the process of hyperspectral image classification,an image classification method based on grid diversity and active learning is proposed.In this method,the principal component space is divided into several grids using the grid method.A sample is randomly selected from each grid containing samples,and the original spectral data is included in the training set.Then,using the active learning method,the K-nearest neighbor method is used to select some samples with the largest uncertainty among the remaining samples and incorporate them into the training set,thereby expanding the training set,making the data set representative,and improving the classification accuracy.In addition,in the process of data processing,principal component analysis and linear discriminant analysis are combined to reduce the dimension of spectral data,which further improves the operation speed.The experimental results show that in the Indian Pines hyperspectral data set,and in the case of a small number of training set samples,the proposed method improves the overall classification accuracy by 12.24%and 19.76%,respectively when compared with random diversity and non-active learning.