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

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

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

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

Recently, it has been proven that noisy label is a pivotal problem to be solved for
hyperspectral image (HSI) classification. In this article, we propose a new noisy label …

The deep learning-based method has shown promising competence in image classification.
Its success can be attributed to the ability to learn discriminative feature representation given …

Semisupervised learning is widely used in hyperspectral image classification to deal with
the limited training samples, however, some more information of hyperspectral image should …

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 …

Hyperspectral image classification is a challenging problem. Among existing approaches to
addressing this problem, the active learning (AL) and semisupervised learning (SSL) …

A novel unsupervised ensemble feature learning method for hyperspectral image
classification is proposed in this study. Firstly, we randomly sample multiple discriminative …