Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …

Recently, unmixing-based networks have shown significant potential in unsupervised
multispectral-aided hyperspectral image super-resolution (MS-aided HS-SR) task …

Recent years have witnessed the flourishing of deep learning-based methods in
hyperspectral anomaly detection (HAD). However, the lack of available supervision …

Accurate semantic segmentation of remote sensing data plays a crucial role in the success
of geoscience research and applications. Recently, multimodal fusion-based segmentation …

By fusing a low-resolution hyperspectral image (LrMSI) with an auxiliary high-resolution
multispectral image (HrMSI), hyperspectral image super-resolution (HISR) can generate a …

Hyperspectral image super-resolution (HSI-SR) can compensate for the incompleteness of
single-sensor imaging and provide desirable products with both high spatial and spectral …

Fusing a low-resolution hyperspectral image (LrHSI) with an auxiliary high-resolution
multispectral image (HrMSI) is a burgeoning technique to realize hyperspectral image super …

Hyperspectral anomaly detection (HAD) aims to identify anomalous targets that deviate from
the surrounding background in unlabeled hyperspectral images (HSIs). Most existing deep …

Recent years have witnessed significant advances of deep learning technology in
hyperspectral anomaly detection (HAD). Among these methods, existing unsupervised …