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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Spatiotemporal Fusion of MODIS and Landsat-7 Reflectance Images via Compressed Sensing
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Spatiotemporal Fusion of MODIS and Landsat-7 Reflectance Images via Compressed Sensing

机译：MODIS和Landsat-7反射率图像的时空融合

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摘要

The fusion of remote sensing images with different spatial and temporal resolutions is needed for diverse Earth observation applications. A small number of spatiotemporal fusion methods that use sparse representation appear to be more promising than weighted- and unmixing-based methods in reflecting abruptly changing terrestrial content. However, none of the existing dictionary-based fusion methods consider the downsampling process explicitly, which is the degradation and sparse observation from high-resolution images to the corresponding low-resolution images. In this paper, the downsampling process is described explicitly under the framework of compressed sensing for reconstruction. With the coupled dictionary to constrain the similarity of sparse coefficients, a new dictionary-based spatiotemporal fusion method is built and named compressed sensing for spatiotemporal fusion, for the spatiotemporal fusion of remote sensing images. To deal with images with a high-resolution difference, typically Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS), the proposed model is performed twice to shorten the gap between the small block size and the large resolution rate. In the experimental procedure, the near-infrared, red, and green bands of Landsat-7 and MODIS are fused with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than five state-of-the-art methods, which prove the feasibility of incorporating the downsampling process in the spatiotemporal model under the framework of compressed sensing.
机译：不同的地球观测应用需要融合具有不同时空分辨率的遥感影像。少数使用稀疏表示的时空融合方法似乎比基于加权和不混合的方法更能反映地面内容的突然变化。但是，现有的基于字典的融合方法均未明确考虑降采样过程，即从高分辨率图像到相应的低分辨率图像的退化和稀疏观察。在本文中，在压缩感知的重建框架下明确描述了下采样过程。利用耦合字典约束稀疏系数的相似性，建立了一种新的基于字典的时空融合方法，并将其命名为时空融合压缩感知，用于遥感影像的时空融合。为了处理具有高分辨率差异的图像（通常是Landsat-7和中分辨率成像光谱仪（MODIS）），建议的模型执行两次以缩短小块尺寸与大分辨率之间的差距。在实验过程中，将Landsat-7和MODIS的近红外，红色和绿色波段与均方根误差融合在一起，以检查预测准确性。从实验中可以得出结论，所提出的方法可以产生比五种最新方法更高的质量，这证明了在压缩感测框架下将下采样过程纳入时空模型的可行性。

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