AI-driven precision agriculture applications can benefit from the large data source that
remote sensing (RS) provides, as it can gather agricultural monitoring data at various scales …

Hyperspectral images (HSIs) are typically utilized in a wide variety of practical applications.
HSI is replete with spatial and spectral information, which provides precise data for material …

Convolutional neural networks (CNNs) have been widely used in hyperspectral image
classification in recent years. The training of CNNs relies on a large amount of labeled …

Due to the advantage of high spectral resolution, hyperspectral imaging techniques have
been extensively used in a variety of fields. Hyperspectral images (HSIs) classification is one …

In this paper, we mainly intend to address the underwater image classification problem in an
open-set scenario. Image classification algorithms have been mostly provided with a small …

Hyperspectral images provide rich spectral information corresponding to visible and near-
infrared imaging regions, facilitating accurate classification, object identification, and target …

The past decade has seen notable impact of man on the environment due to first-time
increase in population and rapid rate of urbanization with the strengthening and …

Hyperspectral images'(HSIs) classification research has seen significant progress with the
use of convolutional neural networks (CNNs) and Transformer blocks. However, these …

The classical Taylor's formula is an elementary tool in mathematical analysis and function
approximation. Its role in the optimization theory, whose data-driven variants have a central …

The traditional Transformer model encounters challenges with variable-length input
sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency …