The performance of deep learning-based denoisers highly depends on the quantity and
quality of training data. However, paired noisy–clean training images are generally …

Deep learning-based hyperspectral image (HSI) change detection (CD) approaches have a
strong ability to leverage spectral-spatial-temporal information through automatic feature …

Time delay and integration (TDI) cameras are widely used in remote sensing areas because
they capture high-resolution and high signal-to-noise ratio (SNR) images and images in low …

In this letter, tensor ring decomposition (TRD) was proposed to estimate the two-dimensional
direction-of-departure (2D-DOD) and two-dimensional direction-of-arrival (2D-DOA) in …

The quality of hyperspectral images seriously impedes subsequent high-level vision tasks
such as image segmentation, image encoding, and target detection. However, the …

One of the significant challenges with traditional single-task learning-based anomaly
detection using noisy hyperspectral images (HSIs) is the loss of anomaly targets during …

Hyperspectral image (HSI) reconstruction from the compressed measurement captured by
the coded aperture snapshot spectral imager system remains a hot topic. Recently, deep …

With the development of remote sensing (RS) technology, single-modal data alone has
gradually become difficult to meet the requirement for high accuracy of RS image …

In this letter, a kernel tensor sparse coding model (KTSCM) is proposed for precise crop
classification of unmanned aerial vehicle (UAV) hyperspectral image (HSI). Benefiting from …

Hyperspectral image (HSI) denoising is an essential preprocessing step for downstream
applications. Although Vision Transformer based approaches show impressive denoising …