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Hyperspectral Image Processing by Jointly Filtering Wavelet Component Tensor
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AI
DOI：10.1109/TGRS.2012.2225065.png)
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摘要
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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.
Keyword:
Classification
denoising
hyperspectral image (HSI)
multiway Wiener filtering (MWF)
signal-dependent noise
wavelet packet transform
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage基于混合空间-光谱导数域小波收缩的高光谱图像降噪
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