An ensemble consists of a set of individually trained classifiers (eg, as neural networks or
decision trees etc.) whose predictions are combined in some manner (eg, averaging or …

Due to the powerful feature information mining ability of deep learning, models such as
convolutional neural network (CNN) and Transformer have gained a certain progress in …

The mining of meaningful features and effective fusion of multisource remote sensing (RS)
data have always been the challenging research problems in the joint classification of …

This paper proposes a Cascaded Random Forest (CRF) method, which can improve the
classification performance by means of combining two different enhancements into the …

Hyperspectral image (HSI) classification plays an important role in the field of remote
sensing. Even though we can easily acquire hyperspectral remote sensing images …

Hyperspectral remote sensing image (HSI) has the characteristics of large data volume and
high spectral resolution. It contains abundant spectral information and has tremendous …

In recent years, convolutional neural networks (CNNs) have been widely used in
hyperspectral images (HSIs) classification. However, 2-D CNN, 3-D CNN, and even the …

Graph learning is an effective manner to analyze the intrinsic properties of data. It has been
widely used in the fields of dimensionality reduction and classification for data. In this paper …

In this paper, we propose a new algorithm for data extraction from time-series data, and
furthermore automatic calculation of highly informative deep features to be used in fault …

A novel sparse tensor dictionary learning algorithm and a convolutional neural network
(CNN) classification method based on this algorithm are proposed for hyperspectral image …