Deep residual learning for remote sensed imagery pansharpening
Deep residual learning for remote sensed imagery pansharpening
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DOI：
10.1109/rsip.2017.7958794
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
Yancong Wei;Qiangqiang Yuan
中科院分区：
文献类型：
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作者：
Yancong Wei;Qiangqiang Yuan
我们提出了一种用于多光谱图像全色锐化的深度卷积网络，以克服传统方法的缺陷并提高融合精度。为了突破深度网络的性能限制，我们应用了针对图像融合任务进行特定调整的残差学习来优化所提出网络的架构。大量实验结果表明，我们的模型能够生成具有最先进质量的高分辨率多光谱图像，因为空间域和光谱域的信息都得到了准确的保留。
We proposed a deep convolutional network for multi-spectral image pan-sharpening to overcome the drawbacks of traditional methods and improve the fusion accuracy. To break the performance limitation of deep networks, residual learning with specific adaption to image fusion tasks is applied to optimize the architecture of proposed network. Results of adequate experiments support that our model can yield high resolution multi-spectral images with state-of-the-art qualities, as the information in both spatial and spectral domains has been accurately preserved.