ABSTRACT
Land cover mapping via classification is one of the most pervasive applications of hyperspectral imagery. Increased availability of hyperspectral data via new sensors and enabling technologies, as well as anticipation of upcoming satellite missions, is inspiring research in classification focused on addressing opportunities and challenges inherent in hyperspectral imagery. This chapter provides an overview of recent advances in classification methods for mapping vegetation using hyperspectral data. High dimensionality and redundant features are long-standing problems, typically addressed by the introduction of front-end feature selection or extraction approaches. A review of traditional methods for feature selection and linear feature extraction is provided, and nonlinear manifold learning is introduced to accommodate nonlinear responses exhibited in hyperspectral data. Spatial variability at multiple scales commonly occurs in both natural vegetation and agricultural croplands, so strategies for accommodating spatial context are reviewed. Non-Gaussian inputs are a common problem in application of traditional parametric classifiers, motivating the development of alternative models and nonparametric classifiers. A summary of Gaussian mixture models and popular nonparametric classification strategies such as support vector machines and deep learning via convolutional neural networks is provided, with illustrative examples. Finally, new frameworks for accommodating classification of multisource, multitemporal, multiscale classification of hyperspectral data are explored. Multikernel learning has been introduced and successfully demonstrated for both single- and multisource inputs with various backend classifiers. Approaches for domain adaptation and transfer learning for classification of multitemporal and multiscale learning are introduced and illustrated. Finally, active and metric learning strategies are outlined as flexible frameworks for mitigating the impact of limited training data on supervised classifiers.
