We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification.
- Authors
Li, Chenming; Qiu, Zelin; Cao, Xueying; Chen, Zhonghao; Gao, Hongmin; Hua, Zaijun; Jeong, Doo Seok
- 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.
- Subjects
CONVOLUTIONAL neural networks; HABITAT suitability index models; CLASSIFICATION
- Publication
Micromachines, 2021, Vol 12, Issue 5, p545
- ISSN
2072-666X
- Publication type
Article
- DOI
10.3390/mi12050545