Classification of Hyperspectral Images

Class-Specific Sparse Multiple Kernel Learning for Spectral–Spatial Hyperspectral Image Classification

CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences

Extended profiles with morphological attribute filters for the analysis of hyperspectral data

Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models

Generalized Composite Kernel Framework for Hyperspectral Image Classification

Graph Convolutional Networks for Hyperspectral Image Classification

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification

More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification

Parsimonious Gaussian Process Models for the Classification of Hyperspectral Remote Sensing Images

Parsimonious Mahalanobis Kernel for the Classification of High Dimensional Data

Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields

SVM-and MRF-Based Method for Accurate Classification of Hyperspectral Images