Recently, the hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have developed rapidly with the advance of deep learning (DL) techniques. In order to more efficiently extract spatial and spectral features, we propose an end-to-end cubic CNN (Cubic-CNN) in this article. The proposed Cubic-CNN is a supervised DL framework that significantly improves classification accuracy and shortens training time. Specifically, Cubic-CNN employs the dimension reduction method combined with principal component analysis and 1-D convolution to remove redundant information from HSIs. Then, convolutions are performed on the planes in different directions of the feature cube data to fully extract spatial and spatial–spectral features and fuse the features from different dimensions. In addition, we performed batch normalization on the data cube after each convolutional layer to improve the performance of the network. Extensive experiments and analysis on standard datasets show that the proposed algorithm can outperform the existing state-of-the-art DL-based methods.

中文翻译：

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一种用于高光谱图像分类的新型三次卷积神经网络

近年来，随着深度学习 (DL) 技术的进步，基于卷积神经网络 (CNN) 的高光谱图像 (HSI) 分类方法得到了迅速发展。为了更有效地提取空间和光谱特征，我们在本文中提出了端到端的立方CNN（Cubic-CNN）。提出的 Cubic-CNN 是一种有监督的 DL 框架，可显着提高分类精度并缩短训练时间。具体来说，Cubic-CNN 采用降维方法结合主成分分析和一维卷积从 HSI 中去除冗余信息。然后，在特征立方体数据的不同方向的平面上进行卷积，以充分提取空间和空间光谱特征，并融合不同维度的特征。此外，我们在每个卷积层之后对数据立方体进行批量归一化，以提高网络的性能。对标准数据集的大量实验和分析表明，所提出的算法可以胜过现有的最先进的基于 DL 的方法。