Hyperspectral Image Processing by Jointly Filtering Wavelet Component Tensor
Hyperspectral Image Processing by Jointly Filtering Wavelet Component Tensor
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DOI：
10.1109/tgrs.2012.2225065
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发表时间：
2013-06-01
影响因子：
8.2
通讯作者：
Bourennane, Salah
中科院分区：
文献类型：
--
作者：
Lin, Tao;Bourennane, Salah
去噪是高光谱成像(HSI)领域中分类和目标检测等应用的重要预处理步骤，以获得良好的性能。由于在新一代高光谱传感器采集的HSI数据中，依赖于信号的光子噪声与电子电路产生的与信号无关的噪声一样占主导地位，因此降低与信号相关的加性光子噪声成为该领域当前研究的重点。为了降低超高速集成电路的光电噪声，本文提出了一种新的方法。首先，提出了一种预白化过程来白化HSI中的噪声。其次，提出了一种张量形式的多维小波包变换(MWPT)以求取HSI的不同分量张量。然后，为了对每个模式下的分量张量进行联合滤波，引入了多路维纳滤波。此外，为了确定MWPT的最佳变换水平和变换基数，提出了一个风险函数。我们的方法在去噪和分类方面的有效性在由机载传感器获取的真实世界的HSI上得到了实验证明。
Denoising is an important preprocessing step for several applications in the hyperspectral imaging (HSI) domain, such as classification and target detection, to achieve good performances. Because the signal-dependent photonic noise has become as dominant as the signal-independent noise generated by the electronic circuitry in HSI data collected by new-generation hyperspectral sensors, the reduction of the additive signal-dependent photonic noise becomes the focus of the current research in this field. To reduce the optoelectronic noise from HSIs, a new method is developed in this paper. First, a prewhitening procedure is proposed to whiten noise in HSIs. Second, a multidimensional wavelet packet transform (MWPT) in tensor form is presented to find different component tensors of the HSI. Then, to jointly filter a component tensor in each mode, a multiway Wiener filter is introduced. Moreover, to determine the best transform level and basis of the MWPT, a risk function is proposed. The effectiveness of our method in denoising and classification is experimentally demonstrated on a real-world HSI acquired by an airborne sensor.