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Resolution enhancement of Hyperion hyperspectral data using Ikonos multispectral data

机译:使用IKONOS多光谱数据分辨率提高Hyperion高光谱数据

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We have developed a new and innovative technique for combining a high-spatial-resolution multispectral image with a lower-spatial-resolution hyperspectral image. The approach, called CRISP, compares the spectral information present in the multispectral image to the spectral content in the hyperspectral image and derives a set of equations to approximately transform the multispectral image into a synthetic hyperspectral image. This synthetic hyperspectral image is then recombined with the original low-spatial-resolution hyperspectral image to produce a sharpened product. The result is a product that has the spectral properties of the hyperspectral image at a spatial resolution approaching that of the multispectral image. To test the accuracy of the CRISP method, we applied the method to synthetic data generated from hyperspectral images acquired with an airborne sensor. These high-spatial-resolution images were used to generate both a lower-spatial-resolution hyperspectral data set and a four-band multispectral data set. With this method, it is possible to compare the output of the CRISP process to the 'truth data' (the original scene). In all of these controlled tests, the CRISP product showed both good spectral and visual fidelity, with an RMS error less than one percent when compared to the 'truth' image. We then applied the method to real world imagery collected by the Hyperion sensor on EO-1 as part of the Hurricane Katrina support effort. In addition to multiple Hyperion data sets, both Ikonos and QuickBird data were also acquired over the New Orleans area. Following registration of the data sets, multiple high-spatial-resolution CRISP-generated hyperspectral data sets were created. In this paper, we present the results of this study that shows the utility of the CRISP-sharpened products to form material classification maps at four-meter resolution from space-based hyperspectral data. These products are compared to the equivalent products generated from the source 30m resolution Hyperion data.
机译:我们开发了一种新的和创新技术,用于将高空间分辨率的多光谱图像与较低空间分辨率的高光谱图像组合。称为清晰的方法,将多光谱图像中存在的光谱信息与高光谱图像中的频谱内容进行比较,并导出一组方程,以大致将多光谱图像变换为合成的高光谱图像。然后将该合成高光谱图像与原始低空间分辨率的高光谱图像重新组合以产生锐化的产品。结果是在接近多光谱图像的空间分辨率下具有高光谱图像的光谱特性的产品。为了测试CRISP方法的准确性,我们将该方法应用于用空气传感器获取的高光谱图像产生的合成数据。这些高空间分辨率图像用于生成低空间分辨率的超细数据集和四频带多光谱数据集。通过这种方法,可以将CRISP过程的输出与“真实的数据”(原始场景)进行比较。在所有这些受控测试中,清晰的产品显示出良好的光谱和视觉保真度,与“真相”图像相比,rms误差小于1%。然后,我们将该方法应用于EO-1上的Hyperion传感器收集的现实世界图像,作为飓风卡特里娜支持努力的一部分。除了多个Hyperion数据集外,还通过新的Orleans区域获取Ikonos和QuickBird数据。在数据集注册之后,创建了多个高空间分辨率清晰的超细数据集。在本文中,我们提出了本研究的结果,该研究显示了脆锐化产品的效用,以四米的超光谱数据以四米分辨率形成材料分类图。将这些产品与从源30M分辨率Hyperion数据产生的等同产品进行比较。

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