Manifold Learning Methods For Hyperspectral Image Classification And Anomaly Detection

The hyperspectral data with high spectral resolution are able to obtain abundant spaial and spectral information, characterize the inherent physical and chemical properties of land cover types, and provide superior capability for discriminating materials than multispectral data. Classification and anomaly detection in hyperspectral data are the focus of this research, which can facilitate understanding the land cover distribution and detecting the interesting targets.The high number of spectral bands, interband spectral redundancy, and the ever presented noise present challenging problems for analysis of hyperspectral data. Moreover, not all the bands are important for understanding the phenomena. Dimensionality reduction is an important preprocessing step for many approaches to analysis of hyperspectral data, which is capable of exploring the inherent low dimensional structure, reducing the computational complexity, and improving the performance of data analysis. Manifold learning is proposed for nonlinear dimensionality reduction. It assumes that the original high dimensional data lie on a low dimensional manifold that can characterize the structure and nonlinear properties of the original data. Since hyperspectral data exhibit intrinsic nonlinearities, the commonly used linear feature extraction methods may lose some important nonlinear properties of hyperspectral data, motivating the research of manifold learning nonlinear dimensionality reduction for hyperspectral data analysis. Manifold learning methods are categorized as global and local techniques. This study focuses on the local manifold learning methods for hyperspectral image classification and anomaly detection. The main work is as follows:(1) The traditional manifold learning methods are restrictedly implemented on training data and lack generalization to new data, and therefore the kernel-based out-of-sample extension method proposed by Bengio is employed. The key point of this approach is to find the kernel function of the specific manifold learning method. Our contribution is to derive the kernel function of local tangent space alignment (LTSA) algorithm and achieve its generalization to new data.(2) For hyperspectral image classification, the paper compares multiple manifold learning methods via the classification using k nearest neighbor (kNN) classifier, with the goal of better understanding the capability of manifold learning for classification and the characteristics of hyperspectral data in the manifold domain. Valuable conclusions are achieved using the experiments implemented on several space-based and airborne hyperspectral data sets. The experimental results demonstrate that the nonlinear manifold learning is promising as dimensionality reduction methods. Its greatest advantage is to discriminate difficult classes in two-category classification problems. Moreover, the supervised local manifold learning methods obtain the best performance and can largely improve the classification.(3) Based on the research of manifold learning in conjunction with the kNN classifier, a new supervised local manifold learning weighted kNN classifier (SLML-WkNN) is proposed and applied to hyperpectral image classification. The weight that is calculated by the kernel function of the specific manifold learning method can capture the geometric properties of each neighborhood and provide a meaningful measure of the contributions of neighbors. The new classifier does not involve dimensionality reduction, and thereby is suitable for large data sets. Further, it can mitigate the influence of imbalanced data sets on kNN classifier.(4) Anomaly detection in hyperspectral images has a problem that the background characteristics may be contaminated by anomalies. Therefore, the robust manifold learning that mitigates the influence of anomalies on background manifold is employed to improve the detection performance. For the robust locally linear embedding (LLE) algorithm, the image is divided into sub-images to reduce the computional complexity. However, this approach cannot obtain the global dimensionality reduced data. Therefore, a background training data selection based robust manifold learning method is then proposed, where the background training data is obtained by the recursive hierachical segmentation. This method can both achieve global data manifold and reduce the complexity. Among the local manifold learning methods, robust LTSA has the best performance.