A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide spectral range. Each band reflects the same scene, composed of various objects imaged at different wavelengths; the spatial information, however, remains generally consistent across bands. Both types of information, spectral and spatial, can be leveraged to identify and classify objects. Recently, the use of machine learning (ML) in object classification has become increasingly widespread. Regardless of the selected approach, object-specific spectral and spatial information is key to discriminating relevant categories. Whereas spatial information is usually repeated across bands, spectral information tends to be distributed more unevenly and often highly so. This poses the issue of removing redundancy, which is commonly called the band selection ( BS ) problem and refers to identifying an optimal subset of bands for further HSI processing.

中文翻译：

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无监督波段选择技术综述：高光谱地球观测数据的土地覆盖分类

高光谱图像 (HSI) 是跨越宽光谱范围的多个窄带图像的集合。每个波段反射同一个场景，由以不同波长成像的各种物体组成；然而，跨波段的空间信息总体上保持一致。光谱和空间这两种类型的信息都可以用来识别和分类对象。最近，机器学习 (ML) 在对象分类中的使用变得越来越普遍。无论选择哪种方法，特定于对象的光谱和空间信息都是区分相关类别的关键。空间信息通常跨波段重复，而光谱信息往往分布更不均匀，而且通常分布更不均匀。这就带来了去除冗余的问题，这通常被称为波段选择（ 学士） 问题，指的是确定用于进一步 HSI 处理的最佳频段子集。