The number of spectral bands obtained by hyperspectral sensors improves the ability to distinguish physical objects and materials. But it also brings new challenges to image classification and analysis. In this study, a novel deep learning-based hybrid model called CNN-CVWNN is presented for the hyperspectral images classification (HSIs). The model uses a convolutional neural network (CNN) to extract multilayer image representation and uses the complex valued wavelet neural network (CVWNN) to classify the image using extracted features. The process steps of the proposed method are briefly as follows. First of all, the CNN algorithm has been applied to hyperspectral images. After this stage, efficient features have been obtained. These extracted features were then converted into a complex-valued number format using a novel random based transformation method. Thus, a novel complex-valued attribute set has been obtained for the HSI classification. The obtained features have been presented as input to the CVWNN algorithm. The hybrid method replaces real valued neural network inside CNN with CVWNN to enhance robustness and generalization of CNN. The experiments have been carried out on three data sets consisted of three popular hyperspectral airborne images. The developed method increases classification accuracy compared to other classification approaches.

高光谱传感器获得的光谱带数量提高了区分物理对象和材料的能力。但这也给图像分类和分析带来了新的挑战。在这项研究中，提出了一种新颖的基于深度学习的混合模型，称为CNN-CVWNN，用于高光谱图像分类（HSI）。该模型使用卷积神经网络（CNN）提取多层图像表示，并使用复值小波神经网络（CVWNN）使用提取的特征对图像进行分类。所提出的方法的处理步骤简要地如下。首先，CNN算法已应用于高光谱图像。在此阶段之后，已经获得了有效的功能。然后使用一种新颖的基于随机的转换方法将这些提取的特征转换为复数值格式。因此，已经获得了用于HSI分类的新颖的复数值属性集。所获得的特征已被呈现为CVWNN算法的输入。混合方法用CVWNN代替CNN内部的实值神经网络，以增强CNN的鲁棒性和泛化性。实验是在由三个流行的高光谱机载图像组成的三个数据集上进行的。与其他分类方法相比，所开发的方法提高了分类准确性。混合方法用CVWNN代替CNN内部的实值神经网络，以增强CNN的鲁棒性和泛化性。实验是在由三个流行的高光谱机载图像组成的三个数据集上进行的。与其他分类方法相比，所开发的方法提高了分类准确性。混合方法用CVWNN代替CNN内部的实值神经网络，以增强CNN的鲁棒性和泛化性。实验是在由三个流行的高光谱机载图像组成的三个数据集上进行的。与其他分类方法相比，所开发的方法提高了分类准确性。