【摘要】 针对在空谱信息特征提取过程中，由于降维造成部分高光谱信息丢失从而影响分类精度的问题，提出一种新型的三维旋转卷积核，并设计了无监督的旋转卷积受限波尔兹曼机。其从三维模型的原始表征中学习三维模型的高层局部特征，在原始数据上直接进行三维特征提取，获取表达力更强的局部表征，从而提高分类精度。将本文所提模型在Indian Pines和Pavia University公开数据集上进行验证，并同其他经典的分类方法进行实验对比。实验结果表明：该方法不仅能大幅度节省可学习的参数，降低模型的复杂度，而且表现出较好的分类性能。更多还原

【Abstract】 In view of the problem that part of hyperspectral information is lost due to dimension reduction in the process of feature extraction of hyperspectral information, which affects the classification accuracy, a novel three-dimensional rotating convolution kernel is proposed, and an unsupervised rotating convolutional restricted Boltzmann machine is designed to learn the high-level local features from the original representation of the 3 D model. We directly extract three-dimensional features from the original data to obtain more expressive local representations, thereby improving classification accuracy. The proposed methods are compared with state-of-the-art methods on the Indian Pines and Pavia University datasets. Experimental results show that the proposed unsupervised learning algorithms, which can extract more effective discriminant features, outperform the state-of-the-art supervised and semi-supervised learning classification methods, and achieve the best accuracy on all of the metrics.更多还原