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Unsupervised methods for the classification of hyperspectral images with low spatial resolution

机译：空间分辨率低的高光谱图像分类的无监督方法

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摘要

Spectral imaging constitutes a fascinating type of data. Organized as a set of grayscale images, with each image recording the reflected light of the same scene under a different light wavelength range, a spectral image can reveal significantly more than a color image. Slight dissimilarities between materials can be easily identified by pinpointing the one or more wavelengths for which the grayscale images expose a contrast. Given this, it is expected that spectral imaging will continue to expand its areas of applicability. Detecting materials in the scene reduces to matching spectra (pixel vectors formed of pixels in the same location throughout the collection of grayscale images) with vectors known to represent the materials.
机译：光谱成像构成了令人着迷的数据类型。组织为一组灰度图像，每个图像记录不同光波长范围内同一场景的反射光，光谱图像可以比彩色图像显示更多的图像。通过精确指出灰度图像暴露对比度的一个或多个波长，可以轻松地识别出材料之间的细微差异。鉴于此，预计光谱成像将继续扩大其适用范围。在场景中检测材料简化为将光谱（在整个灰度图像集合中相同位置的像素所形成的像素矢量）与已知代表该材料的矢量相匹配。

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