Hyperspectral Image Classification Based on Mathematical Morphology and Tensor Decomposition

Abstract

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.

Pattern recognition on FPGA for aerospace applications

“…The authors intend to build this new one approach toward a full parallel model suitable for deep learning paradigm. However differently from the previous works (Nogueira et al, 2021;Gianni et al, 2020;Shen et al, 2019;Mellouli et al, 2019;Hao et al, 2019;Jouni et al, 2020) the authors intend to build a new morphological neural networks based on ELUTs.…”

“…33448/rsd-v10i12.19181 In this first hardware implementation, the work presented only a simple set of morphological operators as a single canonical artificial neuron model (Silva, 1998) when compared to a full deep learning paradigm as the morphological artificial neural networks shown in previous works. (Nogueira et al, 2021;Franchi et al, 2020;Jouni et al, 2020;Shen et al, 2019;Mellouli et al, 2019;Hao et al, 2019). Currently the authors are working on the parallel graph analysis to find main bottlenecks for each operator to increase image access and multiplicity of operators used in order to explore both spatial and temporal parallelism forms (Downton & Crookes, 1998;Johnston et al, 2004).…”

“…In the specialized literature there are also other important papers with contributions from Mathematical Morphology-MM and machine learning hybridization techniques (Nogueira et al, 2021;Franchi et al, 2020;Jouni et al, 2020;Shen et al, 2019;Mellouli et al, 2019;Hao et al, 2019). However, none of these works has implemented in hardware any morphological operators based on Elementary Look-Up Tables-ELUTs paradigm as proposed in Silva (1998) and summarized at Section 2.…”

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

“…In image processing, morphological features are based on both spectral and spatial information involving pixels in the neighborhood. They are widely used in hyperspectral image classification [178,[187][188][189][190][191], noise reduction in lidar [192], building detection [193], and HL-Fusion-based classification [18]. It has been proven that the inclusion of morphological features improves the accuracy in differentiation between roads and buildings [8].…”

Blind Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition

“…It offers the benefit of extremely noise and memory usage reduction, and extracting discriminative features [32]. Accordingly, two well-known tensor decomposition, Tucker decomposition and canonical polyadic decomposition (CPD) are widely used to approximate HR-HSIs [8,26,28,29,37,38]. Learning a low tensor-train rank representation is incorporated in [26] for hyperspectral image super-resolution.…”