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Open AccessArticle

A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening

by Wei Huang 1,*, Jingjing Feng 1, Hua Wang 1 and Le Sun 2
1
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
2
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(4), 242; https://doi.org/10.3390/ijgi9040242
Received: 31 January 2020 / Revised: 5 March 2020 / Accepted: 9 April 2020 / Published: 13 April 2020
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. View Full-Text
Keywords: pan-sharpening; densely connected convolutional network (DenseNet); multi-spectral (MS) pan-sharpening; densely connected convolutional network (DenseNet); multi-spectral (MS)
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MDPI and ACS Style

Huang, W.; Feng, J.; Wang, H.; Sun, L. A New Architecture of Densely Connected Convolutional Networks for Pan-Sharpening. ISPRS Int. J. Geo-Inf. 2020, 9, 242.

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