Convolutional neural networks (CNNs) have achieved remarkable results in multispectral (MS) and panchromatic (PAN) image fusion (pansharpening) because of their strong image feature extraction ability. However, previous CNN-based pansharpening methods mostly use an ordinary convolution, which has a small receptive field in the convolution layer, has insufficient contextual information, and can only extract shallow features, which is not conducive to learning the complex nonlinear mapping relationship between the input image and the fused image. Therefore, this study proposes a pansharpening algorithm based on a multiscale densely convolutional neural network (MDCNN). First, a two-stream network is used for feature extraction, with two convolution layers to extract spectral information from MS images. The multiscale convolutional feature extraction module is designed to extract the spatial detail features of the PAN images. Second, the proposed multiscale densely connected modules and residual modules are used as the backbone of the fusion network. Finally, the deep features generated are reconstructed, and spectral mapping is used to retain spectral information to obtain a high-resolution fusion image. Experimental results using three satellite image datasets show that the proposed algorithm generates high-quality fusion images, and it outperforms most advanced pansharpening methods in subjective visual and objective evaluation indexes.

中文翻译：

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MDCNN：基于多尺度扩张卷积神经网络的多光谱全色锐化

卷积神经网络 (CNN) 由于其强大的图像特征提取能力，在多光谱 (MS) 和全色 (PAN) 图像融合（全色锐化）方面取得了显着的成果。然而，以往基于CNN的pansharpening方法多采用普通卷积，卷积层感受野小，上下文信息不足，只能提取浅层特征，不利于学习复杂的非线性映射关系。输入图像和融合图像。因此，本研究提出了一种基于多尺度密集卷积神经网络（MDCNN）的全色锐化算法。首先，使用双流网络进行特征提取，使用两个卷积层从 MS 图像中提取光谱信息。多尺度卷积特征提取模块旨在提取PAN图像的空间细节特征。其次，提出的多尺度密集连接模块和残差模块用作融合网络的骨干。最后对生成的深层特征进行重构，利用光谱映射保留光谱信息，得到高分辨率的融合图像。使用三个卫星图像数据集的实验结果表明，该算法生成了高质量的融合图像，并且在主观视觉和客观评价指标上优于最先进的全色锐化方法。最后对生成的深层特征进行重构，利用光谱映射保留光谱信息，得到高分辨率的融合图像。使用三个卫星图像数据集的实验结果表明，该算法生成了高质量的融合图像，并且在主观视觉和客观评价指标上优于最先进的全色锐化方法。最后对生成的深层特征进行重构，利用光谱映射保留光谱信息，得到高分辨率的融合图像。使用三个卫星图像数据集的实验结果表明，该算法生成了高质量的融合图像，并且在主观视觉和客观评价指标上优于最先进的全色锐化方法。