Hyperspectral image classification by optimizing convolutional neural networks based on information theory and 3D-Gabor filters

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.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.