Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient

Discriminatively guided filtering (DGF) for hyperspectral image classification

In this paper, we propose a new filtering framework called discriminatively guided image filtering (DGF), for hyperspectral image (HSI) classification. DGF integrates a discriminative classifier and a generative classifier by the guided filtering (GF), considering the complementary strength of these two types of classification paradigms. To demonstrate the effectiveness of the proposed framework, the combination of support vector machine (SVM) and linear discriminative analysis (LDA), which serve as a discriminative classifier and a generative classifier respectively, is investigated in this paper. Specifically, the original HSI is projected into the low-dimensional space induced by LDA to serve as guidance images for filtering the intermediate classification results induced by SVM. Experiment results show the superior performance of the proposed DGF compared with that of the principal component analysis (PCA)-based GF

Building a discriminatively ordered subspace on the generating matrix to classify high-dimensional spectral data

Soft independent modelling of class analogy (SIMCA) is a widely-used subspace method for spectral data classification. However, since the class subspaces are built independently in SIMCA, the discriminative between-class information is neglected. An appealing remedy is to first project the original data to a more discriminative subspace. For this, generalised difference subspace (GDS) that explores the information between class subspaces in the generating matrix can be a strong candidate. However, due to the difference between a class subspace (of infinite scale) and a class (of finite scale), the eigenvectors selected by GDS may not also be discriminative for classifying samples of classes. Therefore in this paper, we propose a discriminatively ordered subspace (DOS): different from GDS, our DOS selects the eigenvectors with high discriminative ability between classes rather than between class subspaces. The experiments on three real spectral datasets demonstrate that applying DOS before SIMCA outperforms its counterparts

Reinforcing Soft Independent Modelling of Class Analogy (SIMCA)

Soft independent modelling of class analogy (SIMCA) is a widely used subspacebased classification technique for spectral data analysis. The principal component (PC) subspace is built for each class separately through principal components analysis (PCA). The squared orthogonal distance (OD2) between the test sample and the class subspace of each class, and the squared score distance (SD2) between the projection of the test sample to the class subspace and the centre of the class subspace, are usually used in the classification rule of SIMCA to classify the test sample. Although it is commonly used to classify high-dimensional spectral data, SIMCA suffers from several drawbacks and some misleading calculations in literature. First, modelling classes separately makes the discriminative between-class information neglected. Second, the literature of SIMCA fail to explore the potential benefit of using geometric convex class models, whose superior classification performance has been demonstrated in face recognition. Third, based on our experiments on several real datasets, calculating OD2 using the formulae in a highlycited SIMCA paper (De Maesschalck et al., 1999) results in worse classification performance than using those in the original SIMCA paper (Wold, 1976) for some high-dimensional data and provides misleading classification results. Fourth, the distance metrics used in the classification rule of SIMCA are predetermined, which are not adapted to different data. Hence the research objectives of my PhD work are to reinforce SIMCA from the following four perspectives: O1) to make its feature space more discriminative; O2) to use geometric convex models as class models in SIMCA for spectral data classification and to study the classification mechanism of classification using different class models; O3) to investigate the equality and inequality of the calculations of OD2 in De Maesschalck et al. (1999) and Wold (1976) for low-dimensional and high-dimensional scenarios; and O4) to make its distance metric adaptively learned from data. In this thesis, we present four contributions to achieve the above four objectives, respectively: First, to achieve O1), we propose to first project the original data to a more discriminative subspace before applying SIMCA. To build such discriminative subspace, we propose the discriminatively ordered subspace (DOS) method, which selects the eigenvectors of the generating matrix with high discriminative ability between classes to span DOS. A paper of this work, “Building a discriminatively ordered subspace on the generating matrix to classify high-dimensional spectral data”, has been recently published by the journal of “Information Sciences”. Second, to achieve O2), we use the geometric convex models, convex hull and convex cone, as class models in SIMCA to classify spectral data. We study the dual of classification methods using three class models: the PC subspace, convex hull and convex cone, to investigate their classification mechanism. We provide theoretical results of the dual analysis, establish a separating hyperplane classification (SHC) framework and provide a new data exploration scheme to analyse the properties of a dataset and why such properties make one or more of the methods suitable for the data. Third, to achieve O3), we compare the calculations of OD2 in De Maesschalck et al. (1999) and Wold (1976). We show that the corresponding formulae in the two papers are equivalent, only when the training data of one class have more samples than features. When the training data of one class have more features than samples (i.e. high-dimensional), the formulae in De Maesschalck et al. (1999) are not precise and affect the classification results. Hence we suggest to use the formulae in Wold (1976) to calculate OD2, to get correct classification results of SIMCA for highdimensional data. Fourth, to achieve O4), we learn the distance metrics in SIMCA based on the derivation of a general formulation of the classification rules used in literature. We define the general formulation as the distance metric from a sample to a class subspace. We propose the method of learning distance to subspace to learn this distance metric by making the samples to be closer to their correct class subspaces while be farther away from their wrong class subspaces. Lastly, at the end of this thesis we append two pieces of work on hyperspectral image analysis. First, the joint paper with Mr Mingzhi Dong and Dr Jing-Hao Xue, “Spectral Nonlocal Restoration of Hyperspectral Images with Low-Rank Property”, has been published by the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Second, the joint paper with Dr Fei Zhou and Dr Jing-Hao Xue, “MvSSIM: A Quality Assessment Index for Hyperspectral Images”, has been in revision for Neurocomputing. As these two papers do not focus on the research objectives of this thesis, they are appended as some additional work during my PhD study

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes