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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Deep Multiscale Detail Networks for Multiband Spectral Image Sharpening
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Deep Multiscale Detail Networks for Multiband Spectral Image Sharpening

机译：多频带光谱图像锐化的深层多尺度细节网络

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摘要

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.
机译：我们介绍了一个新的深度详细信息网络架构，具有分组的多尺寸扩张卷曲，以锐化图像包含多频带光谱信息。具体而言，我们的端到端网络直接熔化低分辨率的多光谱和全色输入，以产生高分辨率的多光谱结果，这是遥感中剪柏路的相同目标。所提出的网络架构是通过利用我们的域知识来设计的，并考虑粉丝的两个目的：光谱和空间保存。对于光谱保存，将上采样的多光谱图像直接添加到输出以进行无损光谱信息传播。对于空间保存，我们在高频域中培训所提出的网络而不是常用的图像域。与传统网络结构不同，我们删除池和批量归一化层以保留空间信息，并分别改善新卫星的泛化。为了在细粒度的级别有效和有效地获得多尺度的上下文特征，我们提出了一种分组的多尺度扩张的网络结构，以扩大每个网络层的接收字段。该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。最终利用这些表示来重建包含平移的空间细节的残差图像。我们培训的网络能够概括不同的卫星图像，而无需参数调整。此外，我们的模型是一般的框架，可以直接用于其他类型的多频光谱图像锐化，例如高光谱图像锐化。实验表明，在定性和定量质量方面，我们的模型对比较的方法进行了有利的反对。

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