Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image.

Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to consider multiple properties of an HSI, which has restricted the discriminant performance of feature representation. In this article, we introduce the hypergraph into semisupervised learning to reveal the complex multistructures of an HSI, and construct a semisupervised discriminant hypergraph learning (SSDHL) method by designing an intraclass hypergraph and an interclass graph with the labeled samples. SSDHL constructs an unsupervised hypergraph with the unlabeled samples. In addition, a total scatter matrix is used to measure the distribution of the labeled and unlabeled samples. Then, a low-dimensional projection function is constructed to compact the properties of the intraclass hypergraph and the unsupervised hypergraph, and simultaneously separate the characteristics of the interclass graph and the total scatter matrix. Finally, according to the objective function, we can obtain the projection matrix and the low-dimensional features. Experiments on three HSI data sets (Botswana, KSC, and PaviaU) show that the proposed method can achieve better classification results compared with a few state-of-the-art methods. The result indicates that SSDHL can simultaneously utilize the labeled and unlabeled samples to represent the homogeneous properties and restrain the heterogeneous characteristics of an HSI

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models

Graph Embedding via High Dimensional Model Representation for Hyperspectral Images

Learning the manifold structure of remote sensing images is of paramount relevance for modeling and understanding processes, as well as to encapsulate the high dimensionality in a reduced set of informative features for subsequent classification, regression, or unmixing. Manifold learning methods have shown excellent performance to deal with hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high-dimensional input space and the (typically low) latent space is linear. This is a particularly strong assumption, especially when dealing with hyperspectral images due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on High Dimensional Model Representation (HDMR) is proposed, which enables to present a nonlinear embedding function to project out-of-sample samples into the latent space. The proposed method is compared to manifold learning methods along with its linear counterparts and achieves promising performance in terms of classification accuracy of a representative set of hyperspectral images.Comment: This is an accepted version of work to be published in the IEEE Transactions on Geoscience and Remote Sensing. 11 page

Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correlation coefficient to obtain multiple fully connected Functional Connectivity brain Networks (FCNs) for every subject, and then learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. • We propose a deep graph learning method to consider graph fusion of graph learning. Specifically, we first employ the Simple Linear Iterative Clustering (SLIC) method to obtain multi-scale features for every image, and then design a new graph fusion method to fine-tune features of every scale. As a result, the multi-scale feature fine-tuning, graph learning, and feature learning are embedded into a unified framework. All proposed methods are evaluated on real-world data sets, by comparing to state-of-the-art methods. Experimental results demonstrate that our methods outperformed all comparison methods

Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification

Pixel-wise hyperspectral image (HSI) classification has been actively studied since it shares similar characteristics with related computer vision tasks, including image classification, object detection, and semantic segmentation, but also possesses inherent differences. The research surrounding HSI classification sheds light on an approach to bridge computer vision and remote sensing. Modern deep neural networks dominate and repeatedly set new records in all image recognition challenges, largely due to their excellence in extracting discriminative features through multi-layer nonlinear transformation. However, three challenges hinder the direct adoption of convolutional neural networks (CNNs) for HSI classification. First, typical HSIs contain hundreds of spectral channels that encode abundant pixel-wise spectral information, leading to the curse of dimensionality. Second, HSIs usually have relatively small numbers of annotated pixels for training along with large numbers of unlabeled pixels, resulting in the problem of generalization. Third, the scarcity of annotations and the complexity of HSI data induce noisy classification maps, which are a common issue in various types of remotely sensed data interpretation. Recent studies show that taking the data attributes into the designing of fundamental components of deep neural networks can improve their representational capacity and then facilitates these models to achieve better recognition performance. To the best of our knowledge, no research has exploited this finding or proposed corresponding models for supervised HSI classification given enough labeled HSI data. In cases of limited labeled HSI samples for training, conditional random fields (CRFs) are an effective graph model to impose data-agnostic constraints upon the intermediate outputs of trained discriminators. Although CRFs have been widely used to enhance HSI classification performance, the integration of deep learning and probabilistic graph models in the framework of semi-supervised learning remains an open question. To this end, this thesis presents supervised spectral-spatial residual networks (SSRNs) and semi-supervised generative adversarial network (GAN) -based models that account for the characteristics of HSIs and make three main contributions. First, spectral and spatial convolution layers are introduced to learn representative HSI features for supervised learning models. Second, generative adversarial networks (GANs) composed of spectral/spatial convolution and transposed-convolution layers are proposed to take advantage of adversarial training using limited amounts of labeled data for semi-supervised learning. Third, fully-connected CRFs are adopted to impose smoothness constraints on the predictions of the trained discriminators of GANs to enhance HSI classification performance. Empirical evidence acquired by experimental comparison to state-of-the-art models validates the effectiveness and generalizability of SSRN, SS-GAN, and GAN-CRF models

Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification

Pixel-wise hyperspectral image (HSI) classification has been actively studied since it shares similar characteristics with related computer vision tasks, including image classification, object detection, and semantic segmentation, but also possesses inherent differences. The research surrounding HSI classification sheds light on an approach to bridge computer vision and remote sensing. Modern deep neural networks dominate and repeatedly set new records in all image recognition challenges, largely due to their excellence in extracting discriminative features through multi-layer nonlinear transformation. However, three challenges hinder the direct adoption of convolutional neural networks (CNNs) for HSI classification. First, typical HSIs contain hundreds of spectral channels that encode abundant pixel-wise spectral information, leading to the curse of dimensionality. Second, HSIs usually have relatively small numbers of annotated pixels for training along with large numbers of unlabeled pixels, resulting in the problem of generalization. Third, the scarcity of annotations and the complexity of HSI data induce noisy classification maps, which are a common issue in various types of remotely sensed data interpretation. Recent studies show that taking the data attributes into the designing of fundamental components of deep neural networks can improve their representational capacity and then facilitates these models to achieve better recognition performance. To the best of our knowledge, no research has exploited this finding or proposed corresponding models for supervised HSI classification given enough labeled HSI data. In cases of limited labeled HSI samples for training, conditional random fields (CRFs) are an effective graph model to impose data-agnostic constraints upon the intermediate outputs of trained discriminators. Although CRFs have been widely used to enhance HSI classification performance, the integration of deep learning and probabilistic graph models in the framework of semi-supervised learning remains an open question. To this end, this thesis presents supervised spectral-spatial residual networks (SSRNs) and semi-supervised generative adversarial network (GAN) -based models that account for the characteristics of HSIs and make three main contributions. First, spectral and spatial convolution layers are introduced to learn representative HSI features for supervised learning models. Second, generative adversarial networks (GANs) composed of spectral/spatial convolution and transposed-convolution layers are proposed to take advantage of adversarial training using limited amounts of labeled data for semi-supervised learning. Third, fully-connected CRFs are adopted to impose smoothness constraints on the predictions of the trained discriminators of GANs to enhance HSI classification performance. Empirical evidence acquired by experimental comparison to state-of-the-art models validates the effectiveness and generalizability of SSRN, SS-GAN, and GAN-CRF models