Hyperspectral image (HSI) in Internet of Things (IoT) is a typical small sample dataset, which
is difficult and costly to label samples manually. In feature extraction, it is difficult to increase …

The realization of efficient classification with limited labeled samples is a critical task in
hyperspectral image classification (HSIC). Convolutional neural networks (CNNs) have …

Pseudo-label (PL) learning-based methods usually regard class confidence above a certain
threshold for unlabeled samples as PLs, which may result in PLs still containing wrong …

The accuracy of hyperspectral image (HSI) classification relies on lots of labeled training
samples. However, the existing HSI data can be used for training with extremely limited …

In this paper, we propose a new framework for hyperspectral image classification, namely
pseudolabel guided kernel learning (PLKL). The proposed framework is capable of fully …

Hyperspectral imagery (HSI) classification is one of the fundamental applications in remote
sensing domain, which aims at predicting the labels of unlabeled pixels in an image with a …

Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI)
classification when abundant labeled samples are available. The problem is that HSI …

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

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 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 …