This article gives a survey of state-of-the-art methods for processing remotely sensed big
data and thoroughly investigates existing parallel implementations on diverse popular high …

Hyperspectral sensors enable the study of the chemical properties of scene materials
remotely for the purpose of identification, detection, and chemical composition analysis of …

Artificial neural networks (ANNs) have been widely used for the analysis of remotely sensed
imagery. In particular, convolutional neural networks (CNNs) are gaining more and more …

AIM: Unilateral sensorineural hearing loss is a brain disease, which causes slight
morphology changes within brain structure. Traditional manual method may ignore this …

Variety identification of seeds is critical for assessing variety purity and ensuring crop yield.
In this paper, a novel method based on hyperspectral imaging (HSI) and deep convolutional …

Rapid and accurate discrimination of Chrysanthemum varieties is very important for
producers, consumers and market regulators. The feasibility of using hyperspectral imaging …

Convolutional neural networks (CNNs) have exhibited extraordinary achievements in
hyperspectral image (HSI) classification due to their detailed representation of features …

Image classification is a very important tool for remotely sensed hyperspectral image
processing. Techniques able to exploit the rich spectral information contained in the data, as …

The storage and processing of remotely sensed hyperspectral images (HSIs) is facing
important challenges due to the computational requirements involved in the analysis of …

Convolutional neural network (CNN)-based hyperspectral image (HSI) classification models
often exhibit high volume and complexity. This not only poses challenges in deploying them …