Unsupervised exploration of hyperspectral and multispectral images

One of the first actions to make in the analysis of hyperspectral and multispectral images is the unsupervised exploration of the spatio-spectral domains. Unsupervised exploration techniques are methods that obtain information about the spatial distribution of compounds on the images, some of their spectral signatures, their main sources of variation, and also help to detect defectuous pixels or spectra, by only using the spatial and spectral information of the images acquired in an unsupervised manner. In this chapter, we present the most popular methods for unsupervised modeling together with examples to understand their major benefits and drawbacks.