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A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening
| Content Provider | MDPI |
|---|---|
| Author | Huang, Wei Feng, Jingjing Wang, Hua Sun, Le |
| Copyright Year | 2020 |
| Description | In this paper, we propose a new architecture of densely connected convolutional networks for pan-sharpening (DCCNP). Since the traditional convolution neural network (CNN) has difficulty handling the lack of a training sample set in the field of remote sensing image fusion, it easily leads to overfitting and the vanishing gradient problem. Therefore, we employed an effective two-dense-block architecture to solve these problems. Meanwhile, to reduce the network architecture complexity, the batch normalization (BN) layer was removed in the design architecture of DenseNet. A new architecture of DenseNet for pan-sharpening, called DCCNP, is proposed, which uses a bottleneck layer and compression factors to narrow the network and reduce the network parameters, effectively suppressing overfitting. The experimental results show that the proposed method can yield a higher performance compared with other state-of-the-art pan-sharpening methods. The proposed method not only improves the spatial resolution of multi-spectral images, but also maintains the spectral information well. |
| Starting Page | 242 |
| e-ISSN | 22209964 |
| DOI | 10.3390/ijgi9040242 |
| Journal | ISPRS International Journal of Geo-Information |
| Issue Number | 4 |
| Volume Number | 9 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-04-13 |
| Access Restriction | Open |
| Subject Keyword | ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Imaging Science Pan-sharpening Densely Connected Convolutional Network (densenet) Multi-spectral (ms) |
| Content Type | Text |
| Resource Type | Article |
