Hyperspectral imaging (HSI) captures rich spectral–spatial cues that enable material identification, target detection, environmental monitoring, and precision agriculture at scale. A new wave of satellite assets—PRISMA, EnMAP, DESIS (ISS), GF-5 AHSI, and HysIS, with ESA CHIME and NASA’s SBG on the horizon—has accelerated data availability and diversity, while pairing naturally with multispectral (Sentinel-2, Landsat-9), LiDAR (e.g., GEDI), thermal (e.g., ECOSTRESS), and SAR (Sentinel-1) for multi-sensor fusion. These advances motivate algorithms that are both efficient and uncertainty-aware, with robust calibration transfer, cross-sensor harmonization, and field/lab validation. Methodologically, progress spans sparse/low-rank models, deep and physics-informed learning, and self-supervised paradigms, enabling strides in anomaly/target detection, change detection, restoration, segmentation, unmixing, classification, and super-resolution. Applications now extend from agriculture and cities to aquatic and cryospheric environments, moving HSI firmly into real-world deployment.

This Research Topic seeks to bring together novel contributions that address the full pipeline of hyperspectral image processing: theoretical advances, computationally efficient algorithms, benchmark dataset development, and translation of research outcomes into actionable applications. Contributions are encouraged from both academia and industry, spanning sensor designers, algorithm developers, and application domain experts.

We welcome Original Research, Methods, and Reviews on (not limited to):

- Algorithmic innovations

- Spectral–spatial feature learning; dimensionality reduction; unmixing with spectral variability

- Anomaly/target and change detection under complex, mixed backgrounds

- Physics-informed, self-/semi-supervised, and uncertainty-aware deep learning

- Transfer learning, domain adaptation, calibration transfer across sensors/missions

- Real-time and energy-efficient inference; scalable distributed pipelines

- Data & benchmarking

- Open datasets (including fusion datasets with MS/LiDAR/thermal/SAR), synthetic data and simulators

- Standardized metrics, uncertainty annotations, cross-sensor harmonization protocols

- Reproducible baselines, open-source code, and experiment checklists

- Applications

- Precision agriculture, forestry, and biodiversity monitoring

- Aquatic/coastal and inland waters (HABs, water quality, bathymetry)

- Cryosphere (snow/ice impurities, albedo, debris-covered glaciers)

- Urban environment, infrastructure, and energy/heat mapping

- Geology, mining, and planetary science

- Disaster assessment, emergency response, and climate services

Are you interested in contributing a manuscript to this Research Topic?

Advances in Hyperspectral Image Processing: From Algorithmic Frameworks to Practical Applications