针对基于光谱信息的分类算法分类精度不佳的问题,提出了高斯线性过程和多邻域优化的高光谱图像分类算法。首先,对原始样本数据进行高斯滤波和线性判别降维处理,然后通过多元逻辑回归模型对数据进行分类,得到数据初始预测标签,再联合局部像元空间位置信息确定预测标签的置信度,通过三层串联的邻域优化层对初预测标签进行校正处理,得到最终的分类结果。将所提算法与其他算法在Indian Pines、Pavia University、Salinas高光谱遥感数据库上进行对比实验,实验结果表明:所提算法在分类精度和时间效率上有更好的性能。

A hyperspectral image classification method is proposed based on a Gaussian linear process and multi-neighborhood optimization to overcome the poor classification accuracy of a classification algorithm based on spectral information. First, Gaussian filtering and linear discriminant dimension reduction are performed on the original sample data; then, the data are classified using a multivariate logistic regression model to obtain their initial prediction labels. Finally, the spatial position information of the local pixels is combined to determine the confidence of these prediction labels, which are corrected by the 3-layer tandem neighborhood optimization to obtain the final classification results. The proposed algorithm is compared with other algorithms on the Indian Pines, Pavia University, and Salinas hyperspectral remote sensing databases, demonstrating the enhanced performance in terms of classification accuracy and time efficiency of the proposed method.