We introduce a new deep detail network architecture with grouped multiscale dilated convolutions to sharpen images contain multiband spectral information. Specifically, our end-to-end network directly fuses low-resolution multispectral and panchromatic inputs to produce high-resolution multispectral results, which is the same goal of the pansharpening in remote sensing. The proposed network architecture is designed by utilizing our domain knowledge and considering the two aims of the pansharpening: spectral and spatial preservations. For spectral preservation, the up-sampled multispectral images are directly added to the output for lossless spectral information propagation. For spatial preservation, we train the proposed network in the high-frequency domain instead of the commonly used image domain. Different from conventional network structures, we remove pooling and batch normalization layers to preserve spatial information and improve generalization to new satellites, respectively. To effectively and efficiently obtain multiscale contextual features at a fine-grained level, we propose a grouped multiscale dilated network structure to enlarge the receptive fields for each network layer. This structure allows the network to capture multiscale representations without increasing the parameter burden and network complexity. These representations are finally utilized to reconstruct the residual images which contain spatial details of PAN. Our trained network is able to generalize different satellite images without the need for parameter tuning. Moreover, our model is a general framework, which can be directly used for other kinds of multiband spectral image sharpening, e.g., hyperspectral image sharpening. Experiments show that our model performs favorably against compared methods in terms of both qualitative and quantitative qualities.

中文翻译：

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用于多波段光谱图像锐化的深度多尺度细节网络。

我们介绍了一种新的深度细节网络体系结构，该体系结构具有分组的多尺度膨胀卷积，可锐化包含多频带频谱信息的图像。具体来说，我们的端到端网络将低分辨率多光谱和全色输入直接融合在一起，以产生高分辨率多光谱结果，这与遥感全彩化的目标相同。拟议的网络体系结构是通过利用我们的领域知识并考虑泛滥的两个目标而设计的：频谱和空间保留。为了保留光谱，将上采样的多光谱图像直接添加到输出中，以进行无损光谱信息传播。对于空间保存，我们在高频域而不是常用的图像域中训练拟议的网络。与传统的网络结构不同，我们删除了池化和批处理归一化层，以保留空间信息并分别改进了对新卫星的推广。为了在细粒度级别上有效且高效地获得多尺度上下文特征，我们提出了一种分组的多尺度扩张网络结构，以扩大每个网络层的接收范围。这种结构允许网络捕获多尺度表示，而不会增加参数负担和网络复杂性。这些表示最终用于重建包含PAN空间细节的残差图像。我们训练有素的网络能够对不同的卫星图像进行概括，而无需进行参数调整。而且，我们的模型是一个通用框架，可以直接用于其他类型的多波段光谱图像锐化，例如 高光谱图像锐化。实验表明，在定性和定量质量方面，我们的模型均优于比较方法。