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Hyperspectral Image Classification With Multidimensional Attribute Profiles
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PRE
AI
DOI：10.1109/LGRS.2015.2443860.png)
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Abstract
En 中文
Morphological profiles have been established during the past decade as one of the principal spatial-spectral pixel description methods. Attribute profiles (APs) in particular have recently emerged as their more efficient generalization, enabling the description of image components through arbitrary parametric features, thus leading to more flexible, complete, and accurate content representations. More precisely, their adaptation to hyperspectral images has been realized through their independent application to an image's bands, after some form of spectral dimension reduction, hence resulting in extended APs. In this letter, a variation of this strategy is explored, consisting of using all of the available image bands simultaneously, during the attribute computation of a connected image component. Thus, the use of a wider array of attributes is enabled, targeting collections of vector pixel values instead of scalars. Specifically, a couple of new multidimensional attributes are investigated, namely, the higher-dimensional spread and higher-dimensional dispersion, describing, respectively, the extent and homogeneity of a multidimensional pixel value distribution. Their practical interest is validated through two common hyperspectral data sets, where they systematically achieve superior classification performance.
Keywords:
Classification
hyperspectral images
mathematical morphology
morphological attribute profiles (APs)
remote sensing
very high resolution images
Journal
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16.4
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1.0W
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5.1K
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