Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification.

Chenming Li,Hongmin Gao,Zelin Qiu,Xueying Cao,Zhonghao Chen,Zaijun Hua

doi:10.3390/mi12050545

Abstract

The convolutional neural network (CNN) has been proven to have better performance in hyperspectral image (HSI) classification than traditional methods. Traditional CNN on hyperspectral image classification is used to pay more attention to spectral features and ignore spatial information. In this paper, a new HSI model called local and hybrid dilated convolution fusion network (LDFN) was proposed, which fuses the local information of details and rich spatial features by expanding the perception field. The details of our local and hybrid dilated convolution fusion network methods are as follows. First, many operations are selected, such as standard convolution, average pooling, dropout and batch normalization. Then, fusion operations of local and hybrid dilated convolution are included to extract rich spatial-spectral information. Last, different convolution layers are gathered into residual fusion networks and finally input into the softmax layer to classify. Three widely hyperspectral datasets (i.e., Salinas, Pavia University and Indian Pines) have been used in the experiments, which show that LDFN outperforms state-of-art classifiers.

Highlights

Micromachines 2021, 12, 545. https://The technology of hyperspectral remote sensing makes full use of high-altitude detection equipment with visible light, infrared light and microwave and other technical methods through information processing and transmission, which can carry out the remote non-contact classification and recognition of ground objects
It is obvious that the accuracies of support vector machines (SVM), 3D-convolutional neural network (CNN) and 3D convolutional auto-encoder (3D-CAE) are less than 95% in the three metrics above
D-CNN with contextual deep CNN framework and spectral-spatial residual network (SSRN) with several residual blocks have more than 95% accuracy in the three metrics

Summary

Introduction

Micromachines 2021, 12, 545. https://The technology of hyperspectral remote sensing makes full use of high-altitude detection equipment with visible light, infrared light and microwave and other technical methods through information processing and transmission, which can carry out the remote non-contact classification and recognition of ground objects. In the early age of hyperspectral image classification, traditional machine learning methods were widely used, for example, support vector machines (SVM) [9,10], k-nearest neighbor (KNN) [11,12], multinomial logistic regression (MLR) [13,14], decision tree [15,16]. Within the same material exist spectral differences in different spaces and different materials may have similar spectral characteristics, so the obtained maps are still noisy due to the limited ability of spatial structure feature extraction. In order to resolve the problem where it is difficult to effectively classify hyperspectral images only by spectral features, many methods of artificial extraction of spatial and spectral features are proposed, for example, Markov random fields (MRFs) [17], generalized composite kernel machine [18]

Methods

Results

Conclusion