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首页> 外文会议>International joint conference on computational intelligence >A Diffusion Approach to Unsupervised Segmentation of Hyper-Spectral Images
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A Diffusion Approach to Unsupervised Segmentation of Hyper-Spectral Images

机译：高光谱图像无监督分割的扩散方法

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摘要

Hyper-spectral cameras capture images at hundreds and even thousands of wavelengths. These hyper-spectral images offer orders of magnitude more intensity information than RGB images. This information can be utilized to obtain segmentation results which are superior to those that are obtained using RGB images. However, many of the wavelengths are correlated and many others are noisy. Consequently, the hyper-spectral data must be preprocessed prior to the application of any segmentation algorithm. Such preprocessing must remove the noise and inter-wavelength correlations and due to complexity constraints represent each pixel by a small number of features which capture the structure of the image. The contribution of this paper is three-fold. First, we utilize the diffusion bases dimensionality reduction algorithm (Schclar and Averbuch in Diffusion bases dimensionality reduction, pp. 151-156, [1]) to derive the features which are needed for the segmentation. Second, we describe a faster version of the diffusion bases algorithm which uses symmetric matrices. Third, we propose a simple algorithm for the segmentation of the dimensionality reduced image. Successful application of the algorithms to hyper-spectral microscopic images and remote-sensed hyper-spectral images demonstrate the effectiveness of the proposed algorithms.
机译：高光谱相机可捕获数百甚至数千个波长的图像。这些高光谱图像提供的强度信息比RGB图像高几个数量级。该信息可用于获得优于使用RGB图像获得的分割结果。但是，许多波长是相关的，许多其他波长是有噪声的。因此，必须在应用任何分割算法之前对高光谱数据进行预处理。这种预处理必须消除噪声和波长间的相关性，并且由于复杂性的限制，每个像素由捕获图像结构的少量特征表示。本文的贡献是三方面的。首先，我们利用扩散基降维算法（Schclar and Averbuch in Diffusion bases降维，第151-156页，[1]）来得出分割所需的特征。其次，我们描述了使用对称矩阵的扩散基算法的更快版本。第三，我们提出了一种用于降维图像分割的简单算法。该算法在高光谱显微图像和遥感高光谱图像上的成功应用证明了所提出算法的有效性。

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