The imbalance between limited labeled pixels and high dimensionality of hyperspectral data
can easily give rise to Hughes phenomenon. Semisupervised learning (SSL) methods …

Hyperspectral imagery (HSI) classification, which attempts to assign hyperspectral pixels
with proper labels, has drawn significant attention in various applications. Recently, the …

Acquiring labeled training examples for hyperspectral images (HSI) is an expensive task,
and even labeling one more pixel requires a real-time field survey of tens of square meters …

Deep learning technologies have been successfully applied to hyperspectral (HS) image
classification with remarkable performance. However, compared with traditional machine …

Label information plays an important role in a supervised hyperspectral image classification
problem. However, current classification methods all ignore an important and inevitable …

As a typical graph-based semisupervised learning technique, the label propagation (LP)
approach has gained much attention in recent years. The key to LP algorithms is the …

Semi-supervised classification methods result in higher performance for hyperspectral
images, because they can utilize the relationship between unlabeled samples and labeled …

Incorrect labeling (ie, noisy label learning) in hyperspectral image (HSI) classification has
attracted so much attention in recent years, which holds the assumption that the given pixels …

This letter presents a semisupervised feature extraction based on collaborative label
propagation (SSCLP) for hyperspectral images (HSIs). SSCLP first proposes a novel …

Graph-based semisupervised hyperspectral image (HSI) classification methods have
obtained extensive attention. In graph-based methods, a graph is first constructed, and then …