A Novel Classification Framework for Hyperspectral Image Data by Improved Multilayer Perceptron Combined with Residual Network

Abstract

Convolutional neural networks (CNNs) have attracted extensive attention in the field of modern remote sensing image processing and show outstanding performance in hyperspectral image (HSI) classification. Nevertheless, some hyperspectral images have fixed position priors and parameter sharing between different positions, so the common convolution layer may ignore some important fine and useful information and cannot guarantee to effectively capture the optimal image features. This paper proposes an improved multilayer perceptron (IMLP) and IMLP combined with ResNet (IMLP-ResNet) two models for HSI classification. Combined with the characteristics of hyperspectral data, we design IMLP based on three improvements. Specifically, a depthwise over-parameterized convolutional layer is introduced to increase learnable parameters of the model in IMLP, which speeds up the convergence of the model without increasing the computational complexity. Secondly, a Focal Loss function is used to suppress the useless ones in the classification task and enhance the critical spectral–spatial features, which allow the IMLP network to learn more useful hyperspectral image information. Furthermore, to enhance the convergence speed of the network, cosine annealing is introduced to further improve the training performance of IMLP. Furthermore, the IMLP module is combined with a residual network (IMLP-ResNet) to construct a symmetric structure, which extracts more advanced semantic information from hyperspectral images. The proposed IMLP and IMLP-ResNet are tested on the two public HSI datasets (i.e., Indian Pines and Pavia University) and a real hyperspectral dataset (Xuzhou). Experimental results demonstrate the superiority of the proposed IMLP-ResNet method over several state-of-the-art methods with the highest OA, which outperforms CNN by 8.19%, 6.28%, 5.59% and outperforms ResNet by 3.52%, 3.54%, 2.67% on Indian Pines, Pavia University and Xuzhou datasets, respectively, and demonstrates that the well-designed MLPs can also obtain remarkable classification performance of HSI.