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

Wei Huang,Le Sun,Jingjing Feng,Hua Wang

doi:10.3390/ijgi9040242

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.

Highlights

IntroductionRemote sensing image analysis has attracted much attention. Greatly successful applications have been achieved in the fields of hyperspectral (HSI) classification [1], anomaly detection [2], HSI unmixing [3], super-resolution [4], pan-sharpening, and so on
In recent years, remote sensing image analysis has attracted much attention
The simulation experiment results were compared with four methods, including the adaptive IHS (AIHS) method [46], the atrous wavelet transform (ATWT)-based method [47], pan-sharpening neural network (PNN) [35], and MSDCNN [38]

Summary

Introduction

Remote sensing image analysis has attracted much attention. Greatly successful applications have been achieved in the fields of hyperspectral (HSI) classification [1], anomaly detection [2], HSI unmixing [3], super-resolution [4], pan-sharpening, and so on. Due to the physical limitations of a single remote sensing imaging device, there is a tradeoff between the spatial and spectral resolution in the remote sensing images [5]. The remote sensing satellites always carry panchromatic sensors and multispectral sensors to simultaneously benefit from both spatial and spectral information, such as QuickBird, IKONOS, and World-view. Multispectral sensors collect multidimensional information, such as spectral and polarization characteristics, while collecting two-dimensional spatial information to obtain multispectral (MS) images with a rich spectrum. The panchromatic sensors capture high spatial resolution panchromatic (PAN) image with one channel, which is very disadvantageous for the recognition and determination of terrain types [6,7,8]. The fused image would have high spatial resolution, and a rich spectrum to achieve the purpose of image enhancement

Methods

Results

Discussion

Conclusion