ABSTRACT

Recently, deep learning approaches, especially convolutional neural networks (CNNs), have been employed for feature extraction (FE) and hyperspectral images (HSIs) classification. The CNN, with all its capabilities, suffers from the input data size and the vast number of parameters, particularly the weights of fully connected (FC) layers. These problems become bottlenecks in real-time systems and cause overfitting in many applications. This paper presents two methods for solving these problems: 1) FE from the input data by applying 3D-Gabor filters, and 2) optimizing the weights of the FC layer based on information theory to decrease the complexity of the FC layer. Traditional 3D-Gabor filters include steerable characteristics that are of interest. Furthermore, they can efficiently extract the spatial features including textures and edges. They consequently provide more generalized and optimized features, which reduce the burden of FE and classification in CNNs. On the other hand, by analysing the weights in the FC layer, from a statistical distribution point of view, each column of the weights follows a coloured Gaussian distribution. Based on this analysis, a method is proposed to optimize the FC layer. The optimization criterion is based on singular value decomposition (SVD) and QR decomposition where Q is an orthogonal matrix and R is a right triangular matrix. The spectral and spatial features are extracted by 3D-Gabor filters. Then, they are classified using CNN which is optimized based on SVD-QR in the training process. The proposed method is tested on Indian Pines, Pavia University, and Kennedy Space Center (KSC) datasets and the quantitative and visual results show the superiority of the proposed method compared to conventional approaches.

摘要

最近，深度学习方法，特别是卷积神经网络（CNN），已用于特征提取（FE）和高光谱图像（HSI）分类。CNN具有所有功能，但会遭受输入数据大小和大量参数（尤其是全连接（FC）层的权重）的困扰。这些问题成为实时系统的瓶颈，并在许多应用中导致过拟合。本文提出了两种解决这些问题的方法：1）通过使用3D-Gabor滤波器从输入数据中进行有限元分析，以及2）基于信息论优化FC层的权重以降低FC层的复杂性。传统的3D-Gabor滤波器包括令人关注的可操纵特性。此外，它们可以有效地提取包括纹理和边缘的空间特征。因此，它们提供了更为通用和优化的功能，从而减轻了CNN中有限元和分类的负担。另一方面，通过从统计分布的角度分析FC层中的权重，权重的每一列都遵循有色高斯分布。在此基础上，提出了一种优化FC层的方法。优化标准基于奇异值分解（SVD）和QR分解，其中Q为正交矩阵，R为直角三角形矩阵。光谱和空间特征由3D-Gabor滤波器提取。然后，在训练过程中使用基于SVD- QR优化的CNN对它们进行分类。该方法在印度松树，帕维亚大学和肯尼迪航天中心（KSC）数据集上进行了测试，定量和可视化结果显示了该方法相对于传统方法的优越性。