A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …

Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

In recent years, convolutional neural networks (CNNs)-based methods achieve cracking
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data
redundancy and improve the performance of hyperspectral image (HSI) classification. A …

In recent years, with the development of deep learning (DL), the hyperspectral image (HSI)
classification methods based on DL have shown superior performance. Although these DL …

We propose a neural network-based feature selection (FS) scheme that can control the level
of redundancy in the selected features by integrating two penalties into a single objective …

Recently mutual information based feature selection criteria have gained popularity for their
superior performances in different applications of pattern recognition and machine learning …

The incorporation of autonomous computation and artificial intelligence (AI) technologies
into smart agriculture concepts is becoming an expected scientific procedure. The airborne …

This paper proposes a novel approach based on adaptive dimensionality reduction (ADR)
and a semi-supervised 3-D convolutional neural network (3-D CNN) for the spectro-spatial …

Band selection is one of the most important technique in the reduction of hyperspectral
image (HSI). Different from traditional feature selection problem, an important characteristic …