A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening
Wei Huang, Jingjing Feng, Hua Wang, Le Sun
Abstract
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.