用深度可分离卷积提升哨兵5号卫星图像分辨率
Depth Separable architecture for Sentinel-5P Super-Resolution
- 采用深度可分离卷积捕捉多光谱通道间相关性
- 在多数波段上优于现有超分辨率方法
- 适合遥感与空气质量监测领域研究者
哨兵5号（Sentinel-5P，S5P）卫星提供大气成分监测数据，具备8个光谱波段，每波段约500个通道。尽管光谱分辨率高，其空间分辨率受限于物理条件。超分辨率（SR）技术可突破这一限制。本文提出专为S5P数据设计的S5-DSCR模型，基于深度可分离卷积（DSC）架构，有效利用跨通道相关性实现空间超分辨率。定量评估显示，该模型在多数波段上优于现有方法。本工作展示了DSC架构在高光谱超分辨率中的潜力，有助于捕捉精细结构，提升空气质量监测与遥感应用精度。
原文摘要 · Abstract (English)
Sentinel-5P (S5P) satellite provides atmospheric measurements for air quality and climate monitoring. While the S5P satellite offers rich spectral resolution, it inherits physical limitations that restricts its spatial resolution. Super-resolution (SR) techniques can overcome these limitations and enhance the spatial resolution of S5P data. In this work, we introduce a novel SR model specifically designed for S5P data that have eight spectral bands with around 500 channels for each band. Our proposed S5-DSCR model relies on Depth Separable Convolution (DSC) architecture to effectively perform spatial SR by exploiting cross-channel correlations. Quantitative evaluation demonstrates that our model outperforms existing methods for the majority of the spectral bands. This work highlights the potential of leveraging DSC architecture to address the challenges of hyperspectral SR. Our model allows for capturing fine details necessary for precise analysis and paves the way for advancements in air quality monitoring as well as remote sensing applications.
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