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Hyperspectral image compression based on tucker decomposition and wavelet transform

机译:基于Tucker分解和小波变换的高光谱图像压缩

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The compression of hyperspectral images becomes recently very attractive issue for remote sensing applications because of the volumetric data. In this paper, an efficient method for hyperspectral image compression is presented based on Tucker Decomposition (TD) and Discrete Wavelet Transform (DWT). The core idea behind our proposed technique is to apply TD on the DWT coefficients of spectral bands of hyperspectral images. Our method not only exploits redundancies between bands but also uses spatial correlation of every image band. Simulation results applied on the real hyperspectral images show a remarkable compression ratio and quality.
机译:由于体积数据,对遥感应用的最近,高光谱图像的压缩变得非常有吸引力。在本文中,基于Tucker分解(TD)和离散小波变换(DWT)来呈现高光谱图像压缩的有效方法。我们所提出的技术背后的核心思想是在高光谱图像的光谱带的DWT系数上应用TD。我们的方法不仅利用频带之间的冗余,还利用每个图像频带的空间相关性。应用于实际高光谱图像上的仿真结果显示出显着的压缩比和质量。

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