Hyperspectral Image (HSI) classification refers to classifying hyperspectral data into features, where labels are given to pixels sharing the same features, distinguishing the present materials of the scene from one another. Naturally a HSI acquires spectral features of pixels, but spatial features based on neighborhood information are also important, which results in the problem of spectral-spatial classification. There are various ways to account to spatial information, one of which is through Mathematical Morphology, which is explored in this work. A HSI is a third-order data block, and building new spatial diversities may increase this order. In many cases, since pixel-wise classification requires a matrix of pixels and features, HSI data are reshaped as matrices which causes high dimensionality and ignores the multi-modal structure of the features. This work deals with HSI classification by modeling the data as tensors of high order. More precisely, multi-modal hyperspectral data is built and dealt with using tensor Canonical Polyadic (CP) decomposition. Experiments on real HSI show the effectiveness of the CP decomposition as a candidate for classification thanks to its properties of representing the pixel data in a matrix compact form with a low dimensional feature space while maintaining the multi-modality of the data.
A Hyperspectral Image (HSI) is an image that is acquired by means of spatial and spectral acquisitions, over an almost continuous spectrum. Pixelwise classification is an important application in HSI due to the natural spectral diversity that the latter brings. There are many works where spatial information (e.g., contextual relations in a spatial neighborhood) is exploited performing a so-called spectral-spatial classification. In this paper, the problem of spectral-spatial classification is addressed in a different manner. First a transformation based on morphological operators is used with an example on additive morphological decomposition (AMD), resulting in a 4-way block of data. The resulting model is identified using tensor decomposition. We take advantage of the compact form of the tensor decomposition to represent the data in order to finally perform a pixelwise classification. Experimental results show that the proposed method provides better performance in comparison to other state-of-the-art methods.
Image classification has been at the core of remote sensing applications. Optical remote sensing imaging systems naturally acquire images with spectral features corresponding to pixels. Spectral classification ignores the spatial distribution of the data which is becoming more relevant with the development of spatial resolution sensors, and many works aim to incorporate spatial features based on neighborhood through for example, Mathematical Morphology (MM). Additionally, one could stack multiple morphological transformations of the image resulting in a highly complex block of data. Since classification is a tool that requires a matrix of samples and features, and simply stacking the different sets of features can lead to the problem of high dimensionality, we propose a way to create a matrix of low dimensional feature space by modeling the data as tensors and thanks to Canonical Polyadic (CP) decomposition. Experiments on real image show the effectiveness of the proposed method.
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