Abstract Machine learning and data mining rely on feature selection to reduce the dimension
of data and increase the performance of algorithms. As a result of such a large search …

The rich spectral information provided by hyperspectral imaging has made this technology
very useful in the classification of remotely sensed data. However, classification of …

The use of hyperspectral imaging sensors has greatly improved the classification of remotely
sensed data because of the abundant spectral information they offer. However, the …

Hyperspectral image (HSI) classification is a classical task in remote sensing image
analysis. With the development of deep learning, schemes based on deep learning have …

Deep learning models have been widely used in hyperspectral images classification.
However, the classification results are not satisfactory when the number of training samples …

Abstract Linear Discriminant Analysis (LDA) is a popular and simple classification tool that
often outperforms more sophisticated modern machine learning techniques in remote …

A class-specific random forest (RF) model with cross-correlation constraints is developed for
the spectral–spatial hyperspectral image (HSI) classification. The novelties of this letter are …

We present a novel, automatic eye gaze tracking scheme inspired by smooth pursuit eye
motion while playing mobile games or watching virtual reality contents. Our algorithm …

Dimensionality Reduction (DR) models are of significance to extract low-dimensional
features for Hyperspectral Images (HSIs) data analysis where there exist lots of noisy and …

Background Hyperspectral imaging (HSI) is an emerging modality for the gross pathology of
the skin. Spectral signatures of HSI could discriminate malignant from benign tissue …