Enhanced Gram-Schmidt Spectral Sharpening Based on Multivariate Regression of MS and Pan Data
Enhanced Gram-Schmidt Spectral Sharpening Based on Multivariate Regression of MS and Pan Data
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DOI：
10.1109/igarss.2006.975
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发表时间：
2006-07
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影响因子：
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通讯作者：
B. Aiazzi;S. Baronti;M. Selva;L. Alparone
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文献类型：
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作者：
B. Aiazzi;S. Baronti;M. Selva;L. Alparone
在这项工作中，一个简单的预处理补丁之前引入的Gram-Schmidt（GS）光谱锐化方法（如在ENVI中实现），从而得到的融合多光谱（MS）数据表现出更高的清晰度和光谱质量。这是通过将广义强度（GI）分量定义为MS波段的加权平均值来实现的，其中权重取为各个波段的光谱响应与全色（Pan）的光谱响应之间的重叠百分比，或者更好地取为MS波段与抽取的Pan图像之间的回归系数。在前一种情况下，为每个传感器预先计算权重。在后一种情况下，通过对正在融合的数据应用多元回归来计算权重。上述GI分量用作平移图像的低分辨率近似。非常高的分辨率IKONOS数据进行的实验结果表明，所提出的增强GS方法在视觉上优于两种模式的ENVI实施GS，特别是在真彩色显示。通过Wald的ERGAS和基于四元数理论的新Q4评分指数等参数对空间退化数据进行定量评分，证实了增强的GS方法优于其基线。
In this work, a simple preprocessing patch is intro- duced before the Gram-Schmidt (GS) spectral sharpening method (as implemented in ENVI) such that the resulting fused multispec- tral (MS) data exhibit higher sharpness and spectral quality. This is achieved by defining a generalized intensity (GI) component as a weighted average of the MS bands, with weights taken either as percentages of overlap between the spectral responses of individual bands and the spectral response of panchromatic (Pan), or better as regression coefficients between the MS bands and the decimated Pan image. In the former case the weights are pre-calculated for each sensor. In the latter case, the weights are calculated by applying a multivariate regression to the data that are being fused. The above GI component is used as low- resolution approximation of the Pan image. Experimental results carried out on very-high resolution IKONOS data demonstrate that the proposed enhanced GS method visually outperforms both modes of the ENVI implementation of GS, especially in true color displays. Quantitative scores performed on spatially degraded data by means of such parameters as Wald's ERGAS and the novel Q4 score index based on quaternions theory, confirm the superiority of the enhanced GS method over its baseline.