Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …

This article aims to investigate how circuit-based hybrid quantum convolutional neural
networks (QCNNs) can be successfully employed as image classifiers in the context of …

Convolutional neural networks (CNNs) can automatically learn features from the
hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features …

Deep learning has achieved promising performance in hyperspectral image (HSI)
classification. Training deep models usually requires labeling massive HSIs, which …

In this paper, a multi-strategy fusion (MSF) framework, based on improved MBLBP and bi-
exponential edge-preserving smoother (BEEPS), is proposed for hyperspectral image (HSI) …

The remarkable growth of deep learning (DL) algorithms in hyperspectral images (HSIs) in
recent years has garnered a lot of research space. This study examines and analyses over …

During the last few years, many deep learning (DL) methods have been proposed for the
classification of hyperspectral images (HSI), and the final results show that these models …

Convolutional neural networks (CNNs) have shown tremendous success for hyperspectral
image classification in recent years. CNNs are capable of capturing multi-scale spectral …

Over last decade, hundreds of deep learning algorithms using CNN, ViT, MLP, and deep
LSTM are proposed to classify hyperspectral remote sensing images with accuracy reaching …