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 …

The quantity and quality of training samples have a great influence on the performance of
most hyperspectral image classification approaches. However, in a real scenario, manually …

Classification is an important technique for remotely sensed hyperspectral image (HSI)
exploitation. Often, the presence of wrong (noisy) labels presents a drawback for accurate …

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 is a rapidly growing and highly active research
area in the field of hyperspectral community. The method that combines both spatial and …

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

Hyperspectral image (HSI) classification (HIC) has attracted much attention in the last
decade. Spectral-spatial HIC methods have been the state-of-the-art methods in recent …

Since it is usually difficult and time-consuming to obtain sufficient training samples by
manually labeling, feature extraction, which investigates the characteristics of hyperspectral …

Hyperspectral image classification is a challenging problem for machine learning methods
due to the small number of labelled samples and high spectral variability. In this paper, to …

Supervised hyperspectral image (HSI) classification has been widely studied and used in
many different applications. However, the performance of the supervised classifiers …