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

被引:23
|
作者
Li, Chenming [1 ]
Qiu, Zelin [1 ]
Cao, Xueying [1 ]
Chen, Zhonghao [1 ]
Gao, Hongmin [1 ]
Hua, Zaijun [1 ]
机构
[1] Hohai Univ, Coll Comp & Informat, Nanjing 211100, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
HSI classification; local and hybrid dilated convolution; residual fusion networks;
D O I
10.3390/mi12050545
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:13
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