ReviewJournal of imaging2019
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.
Review in Journal of imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Hyperspectral Imaging Combined with Multi-Scale Feature Fusion and Improved Faster R-CNN for Automated Detection of Surface Defects on Chicken Carcasses.Foods (Basel, Switzerland) · 2026Article
- Sensing the Action: Rethinking Sensor Modalities and Multi-Modal Fusion in Vision-Language-Action Models for Robotic Manipulation.Sensors (Basel, Switzerland) · 2026Review
- Assessing RGB Color Reliability via Simultaneous Comparison with Hyperspectral Data on PantoneJournal of imaging · 2026Article
- Precision Farming with Smart Sensors: Current State, Challenges and Future Outlook.Sensors (Basel, Switzerland) · 2026Review
- Hyperspectral image and chemometrics. A step beyond classical spectroscopic PAT tools.Analytical and bioanalytical chemistry · 2026Review
- Mould deterioration monitoring of Citri Reticulatae Pericarpium using Vis/NIR imaging and an improved Inception ResNet.Frontiers in plant science · 2026Article
- Deep Architectures Fail to Generalize: A Lightweight Alternative for Agricultural Domain Transfer in Hyperspectral Images.Sensors (Basel, Switzerland) · 2025Article
- Tri branch attention enhanced 3DUNet for remote sensing based hyperspectral image classification.Scientific reports · 2025Article
- Water quality monitoring using hybrid physical-soft sensors for river digital twins: a comprehensive review.Water science and technology : a journal of the International Association on Water Pollution Research · 2025Review
- Dimensionality reduction in hyperspectral imaging using standard deviation-based band selection for efficient classification.Scientific reports · 2025Article
- Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review.Polymers · 2025Review
- Spectral Analysis of Extrahepatic Bile Ducts During Normothermic Liver Machine Perfusion.Bioengineering (Basel, Switzerland) · 2025Article
- RapidFoods (Basel, Switzerland) · 2025Article
- Identification of Gas Mixture Components with Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data.ACS sensors · 2025Article
- Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.The plant genome · 2025Article
- Prediction of post-treatment recurrence in early-stage breast cancer using deep-learning with mid-infrared chemical histopathological imaging.NPJ precision oncology · 2025Article
- Reconstructed hyperspectral imaging forFrontiers in plant science · 2025Article
- Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing.Sensors (Basel, Switzerland) · 2024Article
- Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial-spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.