Feature selection (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …

A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide
spectral range. Each band reflects the same scene, composed of various objects imaged at …

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

Deep convolutional neural networks have been successfully applied to face detection
recently. Despite making remarkable progress, most of the existing detection methods only …

As an effective manner to reduce data redundancy and processing inconvenience,
hyperspectral band selection aims to select a subset of informative and discriminative bands …

High spectral dimensionality of hyperspectral image (HSI) has brought great redundancy for
data processing. Band selection (BS), as one of the most commonly used dimension …

With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose
bands based on band correlation and information has become more significant, but also …

Band selection is an effective method to deal with the difficulties in image transmission,
storage, and processing caused by redundant and noisy bands in hyperspectral images …

This article proposes a multiscale spectral features graph fusion (MSFGF) method for
selecting proper hyperspectral bands. The MSFGF regards that the selected bands should …

Hyperspectral imaging (HSI) classification has drawn tremendous attention in the field of
Earth observation. In the big data era, explosive growth has occurred in the amount of data …