ABSTRACT

MODIS sensors are widely used in a broad range of environmental studies, many of which involve joint analysis of multiple MODIS spectral bands acquired at disparate spatial resolutions. To extract land surface information from multi-resolution MODIS spectral bands, existing studies often downscale lower resolution (LR) bands to match the higher resolution (HR) bands based on simple interpolation or more advanced statistical modeling. Statistical downscaling methods rely on the functional relationship between the LR spectral bands and HR spatial information, which may vary across different land surface types, making statistical downscaling methods less robust. In this paper, we propose an alternative approach based on deep learning to downscale 500 m and 1000 m spectral bands of MODIS to 250 m without additional spatial information. We employ a superresolution architecture based on an encoder decoder network. This deep learning-based method uses a custom loss function and a self-attention layer to preserve local and global spatial relationships of the predictions. We compare our approach with a statistical method specifically developed for downscaling MODIS spectral bands, an interpolation method widely used for downscaling multi-resolution spectral bands, and a deep learning superresolution architecture previously used for downscaling satellite imagery. Results show that our deep learning method outperforms on almost all spectral bands both quantitatively and qualitatively. In particular, our deep learning-based method performs very well on the thermal bands due to the larger scale difference between the input and target resolution. This study demonstrates that our proposed deep learning-based downscaling method can maintain the spatial and spectral fidelity of satellite images and contribute to the integration and enhancement of multi-resolution satellite imagery.

摘要

MODIS 传感器广泛用于广泛的环境研究，其中许多涉及对以不同空间分辨率获取的多个 MODIS 光谱带的联合分析。为了从多分辨率 MODIS 光谱带中提取地表信息，现有的研究通常会根据简单的插值或更高级的统计建模缩小较低分辨率 (LR) 波段以匹配更高分辨率 (HR) 波段。统计降尺度方法依赖于 LR 光谱带和 HR 空间信息之间的函数关系，这可能因不同的地表类型而异，这使得统计降尺度方法不太稳健。在本文中，我们提出了一种基于深度学习的替代方法，可以在没有额外空间信息的情况下将 MODIS 的 500 m 和 1000 m 光谱带缩小到 250 m。我们采用基于编码器解码器网络的超分辨率架构。这种基于深度学习的方法使用自定义损失函数和自注意力层来保留预测的局部和全局空间关系。我们将我们的方法与专门为缩小 MODIS 光谱带而开发的统计方法、广泛用于缩小多分辨率光谱带的插值方法以及以前用于缩小卫星图像的深度学习超分辨率架构进行了比较。结果表明，我们的深度学习方法在数量和质量上都优于几乎所有光谱带。特别是，由于输入和目标分辨率之间的较大尺度差异，我们基于深度学习的方法在热波段上表现非常好。