提出两种图像融合标准化方法，显著提升融合精度。
Standardization for improved Spatio-Temporal Image Fusion
- 采用上采样与基于异常的锐化两种标准化思路。
- 锐化方法使光谱和空间精度分别提升49.46%和78.40%。
- 适合需高精度遥感图像融合的研究者使用。
时空图像融合（STIF）方法通常需要由不同传感器获取的、空间与光谱分辨率匹配的图像集。为促进STIF方法的应用，本文提出并对比了两种标准化方法。第一种基于传统细分辨率图像的上采样；第二种为名为基于异常的卫星图像标准化（ABSIS）的锐化方法，通过融合细分辨率图像序列的整体特征与特定粗分辨率图像的独特属性，生成更接近细分辨率图像聚合结果的图像。两种方法均显著提升了无配对图像块时空融合（USTFIP）方法的精度，其中锐化方法使融合图像的光谱和空间精度分别提升最高达49.46%和78.40%。
原文摘要 · Abstract (English)
Spatio-Temporal Image Fusion (STIF) methods usually require sets of images with matching spatial and spectral resolutions captured by different sensors. To facilitate the application of STIF methods, we propose and compare two different standardization approaches. The first method is based on traditional upscaling of the fine-resolution images. The second method is a sharpening approach called Anomaly Based Satellite Image Standardization (ABSIS) that blends the overall features found in the fine-resolution image series with the distinctive attributes of a specific coarse-resolution image to produce images that more closely resemble the outcome of aggregating the fine-resolution images. Both methods produce a significant increase in accuracy of the Unpaired Spatio Temporal Fusion of Image Patches (USTFIP) STIF method, with the sharpening approach increasing the spectral and spatial accuracies of the fused images by up to 49.46\% and 78.40\%, respectively.
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