The goal of spectral imaging is to capture the spectral signature of a target. Traditional
scanning method for spectral imaging suffers from large system volume and low image …

Hyperspectral data analysis is being utilized as an effective and compelling tool for image
processing, providing unprecedented levels of information and insights for various …

Images captured in low-light environments often suffer from issues related to dark
illumination and damaged details, which results in poor visibility. To address these …

Hyperspectral images (HSIs) are prone to noise because of the imaging mechanism and
environment. This article proposes a multitask sparse representation (MTSR) model-inspired …

Object detection in aerial images is a growing area of research, with maritime object
detection being a particularly important task for reliable surveillance, monitoring, and active …

Deep learning-based hyperspectral image (HSI) classification and object detection
techniques have gained significant attention due to their vital role in image content analysis …

The task of hyperspectral image (HSI) classification is fundamental and crucial in HSI
processing. Currently, domain adaptive methods have become a research hotspot in HSI …

Hyperspectral images (HSI) have a large amount of spectral information reflecting the
characteristics of matter, while their spatial resolution is low due to the limitations of imaging …

Hyperspectral images contain abundant spectral information, which provides great potential
for detecting targets that cannot be analyzed with color images. However, a variety of factors …

Hyperspectral (HS) pansharpening consists of fusing a high-resolution panchromatic (PAN)
band and a low-resolution HS image to obtain a new image with high resolution in both the …