Pan-sharpening is an effective method to obtain high-resolution multispectral (MS) images by fusing panchromatic (PAN) images with fine spatial structure and low-resolution MS images with rich spectral information. Many methods have been recently developed based on convolutional neural networks (CNNs) for pan-sharpening, which try to achieve better fusion performance using a large amount of training data. The huge demand for data of CNN-based methods results in huge pressure on transmission and storage conditions. Especially, PAN images occupy the main amount of data space because of whose spatial resolution is usually four times that of the corresponding MS images, which limits the application of pan-sharpening. We propose a CNN-based pan-sharpening method for compressed remote sensing to overcome this limitation, which can achieve pan-sharpening based on compressed data while eliminating compression artifacts. Specifically, we use compressed PAN images as the input and remove the compression artifacts, then fuse the PAN and MS images to achieve pan-sharpening. This method can greatly reduce the data capacity by data compression and ensure the pan-sharpening performance. Compared with the widely used state-of-the-art pan-sharpening approaches in a comprehensive evaluation, our method can obtain more promising results on compressed remote sensing images, and its superiority is thus demonstrated.

中文翻译：

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压缩遥感图像的全色锐化

全色锐化是一种通过融合具有精细空间结构的全色 (PAN) 图像和具有丰富光谱信息的低分辨率 MS 图像来获得高分辨率多光谱 (MS) 图像的有效方法。最近开发了许多基于卷积神经网络 (CNN) 的全色锐化方法，试图使用大量训练数据实现更好的融合性能。基于CNN的方法对数据的巨大需求导致传输和存储条件的巨大压力。特别是PAN图像占据了主要的数据空间，因为其空间分辨率通常是相应MS图像的四倍，这限制了pan-sharpening的应用。我们提出了一种用于压缩遥感的基于 CNN 的全色锐化方法来克服这一限制，它可以在消除压缩伪影的同时实现基于压缩数据的全色锐化。具体来说，我们使用压缩的 PAN 图像作为输入并去除压缩伪影，然后融合 PAN 和 MS 图像以实现全色锐化。这种方法可以通过数据压缩大大减少数据容量，保证全色锐化性能。与综合评估中广泛使用的最先进的全色锐化方法相比，我们的方法可以在压缩遥感图像上获得更有希望的结果，从而证明了其优越性。这种方法可以通过数据压缩大大减少数据容量，保证全色锐化性能。与综合评估中广泛使用的最先进的全色锐化方法相比，我们的方法可以在压缩遥感图像上获得更有希望的结果，从而证明了其优越性。这种方法可以通过数据压缩大大减少数据容量，保证全色锐化性能。与综合评估中广泛使用的最先进的全色锐化方法相比，我们的方法可以在压缩遥感图像上获得更有希望的结果，从而证明了其优越性。