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首页> 外文会议>Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XX >Spectral Image Fusion using Band Reduction and Contourlets
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Spectral Image Fusion using Band Reduction and Contourlets

机译：使用带减少和轮廓波的光谱图像融合

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摘要

Spectral images have relatively low spatial resolution, compared to high-resolution single band panchromatic (PAN) images. Therefore, fusing a spectral image with a PAN image has been widely studied to produce a high-resolution spectral image. However, raw spectral images are too large to process and contain redundant information that is not utilized in the fusion process. In this study, we propose a novel fusion method that employs a spectral band reduction and contourlets. The band reduction begins with the best two band combination, and this two-band combination is subsequently augmented to three, four, and more until the desired number of bands is selected. The adopted band selection algorithm using the endmember extraction concept employs a sequential forward search strategy. Next, the image fusion is performed with two different spectral images based on the frequency components that are newly obtained by contourlet transform (CT). One spectral image that is used as a dataset is multispectral (MS) image and the other is hyperspectral (HS) image. Each original spectral image is pre-processed by spectrally integrating over the entire spectral range to obtain a PAN source image that is used in the fusion process. This way, we can eliminate the step of image co-registration since the obtained PAN image is already perfectly aligned to the spectral image. Next, we fuse the band-reduced spectral images with the PAN images using contourlet-based fusion framework. The resultant fusion image provides enhanced spatial resolution while preserving the spectral information. In order to analyze the band reduction performance, the original spectral images are fused with the same PAN images to serve as a reference image, which is then compared to the band-reduced spectral image fusion results using six different quality metrics.
机译：与高分辨率单波段全色（PAN）图像相比，光谱图像具有相对较低的空间分辨率。因此，已经广泛地研究了将光谱图像与PAN图像融合以产生高分辨率光谱图像。但是，原始光谱图像太大，无法处理，并且包含在融合过程中未使用的冗余信息。在这项研究中，我们提出了一种新颖的融合方法，该方法采用了谱带减小和轮廓波。频段减少从最佳的两个频段组合开始，然后将此两个频段组合增加到三个，四个或更多个，直到选择了所需的频段数量。采用的采用端成员提取概念的频带选择算法采用了顺序前向搜索策略。接下来，基于通过轮廓波变换（CT）新获得的频率分量，使用两个不同的光谱图像执行图像融合。一个用作数据集的光谱图像是多光谱（MS）图像，另一个是高光谱（HS）图像。通过在整个光谱范围内进行光谱积分，对每个原始光谱图像进行预处理，以获得在融合过程中使用的PAN源图像。这样，由于获得的PAN图像已经与光谱图像完美对齐，因此可以消除图像共配准的步骤。接下来，我们使用基于Contourlet的融合框架将减少波段的光谱图像与PAN图像融合。所得融合图像在保留光谱信息的同时提供增强的空间分辨率。为了分析频带减少性能，将原始光谱图像与相同的PAN图像融合以用作参考图像，然后使用六个不同的质量度量将其与减少频带的光谱图像融合结果进行比较。

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