Hyperspectral and Multispectral Image Fusion using Optimized Twin Dictionaries

Spectral or spatial dictionary has been widely used in fusing low-spatial-resolution hyperspectral (LH) images and high-spatial-resolution multispectral (HM) images. However, only using spectral dictionary is insufficient for preserving spatial information, and vice versa. To address this problem, a new LH and HM image fusion method termed OTD using optimized twin dictionaries is proposed in this paper. The fusion problem of OTD is formulated analytically in the framework of sparse representation, as an optimization of twin spectral-spatial dictionaries and their corresponding sparse coefficients. More specifically, the spectral dictionary representing the generalized spectrums and its spectral sparse coefficients are optimized by utilizing the observed LH and HM images in the spectral domain; and the spatial dictionary representing the spatial information and its spatial sparse coefficients are optimized by modeling the rest of high-frequency information in the spatial domain. In addition, without non-negative constraints, the alternating direction methods of multipliers (ADMM) are employed to implement the above optimization process. Comparison results with the related state-of-the-art fusion methods on various datasets demonstrate that our proposed OTD method achieves a better fusion performance in both spatial and spectral domains

Spectral Super-resolution for RGB Images using Class-based BP Neural Networks

Hyperspectral images are of high spectral resolution and have been widely used in many applications, but the imaging process to achieve high spectral resolution is at the expense of spatial resolution. This paper aims to construct a high-spatial-resolution hyperspectral (HHS) image from a high-spatial-resolution RGB image, by proposing a novel class-based spectral super-resolution method. With the help of a set of RGB and HHS image-pairs, our proposed method learns nonlinear spectral mappings between RGB and HHS image-pairs using class-based back propagation neural networks (BPNNs). In the training stage, unsupervised clustering is used to divide an RGB image into several classes according to spectral correlation, and the spectrum-pairs from the classified RGB images and the corresponding HHS images are used to train the BPNNs, to establish the nonlinear spectral mapping for each class. In the spectral super-resolution stage, a supervised classification is used to classify the given RGB image into the classes determined during the training stage, and the final HHS image is reconstructed from the classified given RGB image using the trained BPNNs. Comparisons on three standard datasets, ICVL, CAVE and NUS, demonstrate that, our proposed method achieves a better spectral super-resolution quality than related state-of-the-art methods

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Subspace Support Vector Data Description and Extensions

Machine learning deals with discovering the knowledge that governs the learning process. The science of machine learning helps create techniques that enhance the capabilities of a system through the use of data. Typical machine learning techniques identify or predict different patterns in the data. In classification tasks, a machine learning model is trained using some training data to identify the unknown function that maps the input data to the output labels. The classification task gets challenging if the data from some categories are either unavailable or so diverse that they cannot be modelled statistically. For example, to train a model for anomaly detection, it is usually challenging to collect anomalous data for training, but the normal data is available in abundance. In such cases, it is possible to use One-Class Classification (OCC) techniques where the model is trained by using data only from one class. OCC algorithms are practical in situations where it is vital to identify one of the categories, but the examples from that specific category are scarce. Numerous OCC techniques have been proposed in the literature that model the data in the given feature space; however, such data can be high-dimensional or may not provide discriminative information for classification. In order to avoid the curse of dimensionality, standard dimensionality reduction techniques are commonly used as a preprocessing step in many machine learning algorithms. Principal Component Analysis (PCA) is an example of a widely used algorithm to transform data into a subspace suitable for the task at hand while maintaining the meaningful features of a given dataset. This thesis provides a new paradigm that jointly optimizes a subspace and data description for one-class classification via Support Vector Data Description (SVDD). We initiated the idea of subspace learning for one class classification by proposing a novel Subspace Support Vector Data Description (SSVDD) method, which was further extended to Ellipsoidal Subspace Support Vector Data Description (ESSVDD). ESSVDD generalizes SSVDD for a hypersphere by using ellipsoidal data description and it converges faster than SSVDD. It is important to train a joint model for multimodal data when data is collected from multiple sources. Therefore, we also proposed a multimodal approach, namely Multimodal Subspace Support Vector Data Description (MSSVDD) for transforming the data from multiple modalities to a common shared space for OCC. An important contribution of this thesis is to provide a framework unifying the subspace learning methods for SVDD. The proposed Graph-Embedded Subspace Support Vector Data Description (GESSVDD) framework helps revealing novel insights into the previously proposed methods and allows deriving novel variants that incorporate different optimization goals. The main focus of the thesis is on generic novel methods which can be adapted to different application domains. We experimented with standard datasets from different domains such as robotics, healthcare, and economics and achieved better performance than competing methods in most of the cases. We also proposed a taxa identification framework for rare benthic macroinvertebrates. Benthic macroinvertebrate taxa distribution is typically very imbalanced. The amounts of training images for the rarest classes are too low for properly training deep learning-based methods, while these rarest classes can be central in biodiversity monitoring. We show that the classic one-class classifiers in general, and the proposed methods in particular, can enhance a deep neural network classification performance for imbalanced datasets

Adaptive Similarity Measures for Material Identification in Hyperspectral Imagery

Remotely-sensed hyperspectral imagery has become one the most advanced tools for analyzing the processes that shape the Earth and other planets. Effective, rapid analysis of high-volume, high-dimensional hyperspectral image data sets demands efficient, automated techniques to identify signatures of known materials in such imagery. In this thesis, we develop a framework for automatic material identification in hyperspectral imagery using adaptive similarity measures. We frame the material identification problem as a multiclass similarity-based classification problem, where our goal is to predict material labels for unlabeled target spectra based upon their similarities to source spectra with known material labels. As differences in capture conditions affect the spectral representations of materials, we divide the material identification problem into intra-domain (i.e., source and target spectra captured under identical conditions) and inter-domain (i.e., source and target spectra captured under different conditions) settings. The first component of this thesis develops adaptive similarity measures for intra-domain settings that measure the relevance of spectral features to the given classification task using small amounts of labeled data. We propose a technique based on multiclass Linear Discriminant Analysis (LDA) that combines several distinct similarity measures into a single hybrid measure capturing the strengths of each of the individual measures. We also provide a comparative survey of techniques for low-rank Mahalanobis metric learning, and demonstrate that regularized LDA yields competitive results to the state-of-the-art, at substantially lower computational cost. The second component of this thesis shifts the focus to inter-domain settings, and proposes a multiclass domain adaptation framework that reconciles systematic differences between spectra captured under similar, but not identical, conditions. Our framework computes a similarity-based mapping that captures structured, relative relationships between classes shared between source and target domains, allowing us apply a classifier trained using labeled source spectra to classify target spectra. We demonstrate improved domain adaptation accuracy in comparison to recently-proposed multitask learning and manifold alignment techniques in several case studies involving state-of-the-art synthetic and real-world hyperspectral imagery