Name
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) have recently shown outstanding capability for hyperspectral image (HSI) classification. In this work, a novel CNN model is proposed, which is wider than other existing deep learning-based HSI classification models. Based on the fact that very deep residual networks (ResNets) behave like ensembles of relatively shallow networks, our proposed network, called multipath ResNet (MPRN), employs multiple residual functions in the residual blocks to make the network wider, rather than deeper. The proposed network consists of shorter-medium paths for efficient gradient flow and replaces the stacking of multiple residual blocks in ResNet with fewer residual blocks but more parallel residual functions in each of it. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art classification methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/rs11161896 | REMOTE SENSING |
Keywords | Field | DocType |
hyperspectral image (HSI) classification,convolutional neural network (CNN),deep learning,residual network (ResNet),ensemble | Hyperspectral image classification,Multipath propagation,Computer vision,Residual,Remote sensing,Artificial intelligence,Geology | Journal |
Volume | Issue | Citations |
11 | 16 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Meng | 1 | 0 | 0.34 |
| Lingling Li | 2 | 1 | 1.70 |
| Xu Tang | 3 | 22 | 10.14 |
| Zhixi Feng | 4 | 61 | 7.25 |
| Licheng Jiao | 5 | 5698 | 475.84 |
| Miaomiao Liang | 6 | 0 | 0.68 |