无需辅助数据，通过校正采样模糊提升高光谱图像分辨率
Hyperspectral Spatial Super-Resolution using Keystone Error
- 用盲反卷积估计高分辨率点扩散函数
- 在HySIS传感器上实现约1.3倍分辨率提升
- 适合无辅助数据的高光谱超分辨场景
高光谱图像通过精细光谱分辨率捕捉地物光谱特征，有助于精确识别。虽然高空间分辨率能进一步增强这一能力，但依赖大望远镜等硬件提升成本高且效率低。更优方案是通过地面处理技术（如超锐化）融合高光谱与高空间分辨率数据。然而，该方法在不同时间采集的数据间效果受限。本文提出一种无需辅助输入的超分辨率方法：利用盲反卷积估计高分辨率点扩散函数（PSF），并基于模型的超分辨率框架校正采样相关模糊，无需假设已知高分辨率模糊。同时引入自适应先验，性能优于现有方法。应用于印度空间研究组织（ISRO）的HySIS可见光与近红外（VNIR）光谱仪数据，可有效消除混叠，使分辨率提升约1.3倍。该方法通用性强，适用于类似系统。
原文摘要 · Abstract (English)
Hyperspectral images enable precise identification of ground objects by capturing their spectral signatures with fine spectral resolution.While high spatial resolution further enhances this capability, increasing spatial resolution through hardware like larger telescopes is costly and inefficient. A more optimal solution is using ground processing techniques, such as hypersharpening, to merge high spectral and spatial resolution data. However, this method works best when datasets are captured under similar conditions, which is difficult when using data from different times. In this work, we propose a superresolution approach to enhance hyperspectral data's spatial resolution without auxiliary input. Our method estimates the high-resolution point spread function (PSF) using blind deconvolution and corrects for sampling-related blur using a model-based superresolution framework. This differs from previous approaches by not assuming a known highresolution blur. We also introduce an adaptive prior that improves performance compared to existing methods. Applied to the visible and near-infrared (VNIR) spectrometer of HySIS, ISRO hyperspectral sensor, our algorithm removes aliasing and boosts resolution by approximately 1.3 times. It is versatile and can be applied to similar systems.
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