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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
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
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;
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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