针对高斯过程回归在高光谱图像分类中计算量较大、分类精度较低等问题, 提出一种基于改进高斯过程回归的高光谱空谱联合分类算法。算法以最大方差为指标选取样本的子集缩小高斯过程回归参数求解的计算范围,采用平方根矩阵分解法对新添加样本进行模型结果预测, 有效提升运算效率; 算法以空间-光谱特征信息为基础, 在像元近邻空间中重新定义邻域像元空-谱关联距离, 将融入空间近邻信息的空-谱关联距离作为权值来度量邻域像元相似性, 加大同类地物归为近邻的概率, 从而提高地物分类的精度。在Indian Pines和Pavia University两组高光谱数据集上进行仿真实验, 实验结果可知, 与其他同类算法横向相比, 本文提出的改进算法在总体分类精度、平均分类精度和Kappa系数等评价指标至少提高了2.3%, 1.4%和1.07%, 与改进前的模型算法纵向对比可知, 本文提出的改进算法在取得较高总体分类精度的同时, 大幅降低了算法的运行时间。

To solve the problems of high calculation amounts and low classification accuracy of Gaussian process regression in hyperspectral image classification, a spectral-spatial joint classification algorithm for hyperspectral images based on improved Gaussian process regression was proposed. A subset of samples was selected using maximum variance as the index to narrow the calculation range of the Gaussian process regression parameter solution, and a square root matrix decomposition method was introduced to predict the model results for incoming added samples, all of which effectively improve the efficiency of calculation. A spatial-spectral correlation distance of neighborhood pixels was redefined in the pixel neighbor space based on spatial-spectral feature information. In addition, a space-spectrum correlation distance integrated with spatial neighbor information was used as the weight to measure the similarity of neighborhood pixels. These increase the probability that similar features would be classified as neighbors, thus improving the accuracy of feature classification. Simulation experiments were conducted on two sets of hyperspectral datasets from Indian Pines and Pavia University. Experimental results show that, compared with other similar algorithms, the proposed algorithm improves overall classification accuracy, average classification accuracy, and the Kappa coefficient by at least 2.3%, 1.4%, and 1.07%, respectively. Compared with the model algorithm prior to enhancements, the improved algorithm not only achieves higher overall classification accuracy but also considerably reduces the running time.