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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Wavelet-Based Multicomponent Denoising Profile for the Classification of Hyperspectral Images
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Wavelet-Based Multicomponent Denoising Profile for the Classification of Hyperspectral Images

机译：基于小波的多组分去噪剖面，用于高光谱图像的分类

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摘要

The high resolution of the hyperspectral remote sensing images available allows the detailed analysis of even small spatial structures. As a consequence, the study of techniques to efficiently extract spatial information is a very active realm. In this paper, we propose a novel denoising wavelet-based profile for the extraction of spatial information that does not require parameters fixed by the user. Over each band obtained by a wavelet-based feature extraction technique, a denoising profile (DP) is built through the recursive application of discrete wavelet transforms followed by a thresholding process. Each component of the DP consists of features reconstructed by recursively applying inverse wavelet transforms to the thresholded coefficients. Several thresholding methods are explored. In order to show the effectiveness of the extended DP (EDP), we propose a classification scheme based on the computation of the EDP and supervised classification by extreme learning machine. The obtained results are compared to other state-of-the-art methods based on profiles in the literature. An additional study of behavior in the presence of added noise is also performed showing the high reliability of the EDP proposed.
机译：可用于高光谱遥感图像的高分辨率允许甚至小空间结构进行详细分析。结果，对有效提取空间信息的技术的研究是非常活跃的领域。在本文中，我们提出了一种基于新的基于小波的简档，用于提取不需要用户固定的参数的空间信息。在由基于小波的特征提取技术获得的每个频带上，通过离散小波变换的递归施加，然后是阈值处理来构建去噪分布（DP）。 DP的每个组件包括通过递归向逆小波变换重建的特征，该特征对阈值的系数。探讨了几种阈值化方法。为了展示扩展DP（EDP）的有效性，我们提出了一种基于EDP计算和极端学习机的监督分类的分类方案。将获得的结果与基于文献中的曲线的其他最新方法进行比较。还执行了对添加噪声的存在中的行为的额外研究，示出了所提出的EDP的高可靠性。

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