Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

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 …

The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …

Band selection refers to the process of choosing the most relevant bands in a hyperspectral
image. By selecting a limited number of optimal bands, we aim at speeding up model …

Several versions of convolutional neural network (CNN) were developed to classify
hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral …

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 …

Deep convolutional neural networks (CNN) have led to a successful breakthrough for
hyperspectral image (HSI) classification. In this paper, a CNN system embedded with an …

Hyperspectral image (HSI), with hundreds of narrow and adjacent spectral bands, supplies
plentiful information to distinguish various land-cover types. However, these spectral bands …

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 …

Outlier detection can identify anomalies in large-scale data. To provide reliability and
security for Internet of Things (IoT)-enabled maritime transportation systems (MTSs), in this …