用10米数据提升30米卫星影像分辨率，实现多源异构影像精准对齐。
Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery
- 以HLS10为参考，对齐并上采样HLS30影像，解决跨传感器分辨率差异问题。
- 在真实卫星数据上验证，显著提升30米影像的细节还原能力。
- 适用于多源卫星影像融合，为地理空间分析提供高质量输入。
高分辨率卫星影像对地理空间分析至关重要，但不同卫星传感器间存在空间分辨率差异，给数据融合与下游应用带来挑战。超分辨率技术可缓解这一差距，但现有方法依赖人工下采样的图像，而非真实传感器数据，且不适用于具有不同光谱、时间特性的异构卫星传感器。本文提出一种初步框架，利用同属HLS数据集的HLS10（10米）作为参考，对HLS30（30米）影像进行对齐与上采样。该方法旨在弥合两类传感器间的分辨率差距，提升陆地卫星影像的重建质量。定量与定性评估表明，所提方法有效，具备提升基于卫星感知应用潜力。本研究揭示了异构卫星影像超分辨率的可行性，并指明未来发展的关键方向。
原文摘要 · Abstract (English)
High-resolution satellite imagery is essential for geospatial analysis, yet differences in spatial resolution across satellite sensors present challenges for data fusion and downstream applications. Super-resolution techniques can help bridge this gap, but existing methods rely on artificially downscaled images rather than real sensor data and are not well suited for heterogeneous satellite sensors with differing spectral, temporal characteristics. In this work, we develop a preliminary framework to align and upscale Harmonized Landsat Sentinel 30m(HLS 30) imagery using Harmonized Landsat Sentinel 10m(HLS10) as a reference from the HLS dataset. Our approach aims to bridge the resolution gap between these sensors and improve the quality of super-resolved Landsat imagery. Quantitative and qualitative evaluations demonstrate the effectiveness of our method, showing its potential for enhancing satellite-based sensing applications. This study provides insights into the feasibility of heterogeneous satellite image super-resolution and highlights key considerations for future advancements in the field.
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