ReviewComputational intelligence and neuroscience2022
Hyperspectral Image Classification: Potentials, Challenges, and Future Directions.
Review in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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Who cites it
12 citing papers in PubMed.
- IL-HS: a deep inception-LSTM architecture for enhanced lithological mapping using EnMAP hyperspectral remote sensing data.Scientific reports · 2026Article
- Hybrid Mamba-Graph Fusion with Multi-Stage Pseudo-Label Refinement for Semi-Supervised Hyperspectral-LiDAR Classification.Sensors (Basel, Switzerland) · 2026Article
- Collaborative representation and confidence-driven semi-supervised learning for hyperspectral image classification.Scientific reports · 2026Article
- Design and Demonstration of Compact and Lightweight Imaging Spectrometer Based on Schwarzschild Reflector Systems Using Commercial Off-the-Shelf Optics.Sensors (Basel, Switzerland) · 2025Article
- SGFNet: Redundancy-Reduced Spectral-Spatial Fusion Network for Hyperspectral Image Classification.Entropy (Basel, Switzerland) · 2025Article
- CViT Weakly Supervised Network Fusing Dual-Branch Local-Global Features for Hyperspectral Image Classification.Entropy (Basel, Switzerland) · 2025Article
- 3D Hyperspectral Data Analysis with Spatially Aware Deep Learning for Diagnostic Applications.Analytical chemistry · 2025Article
- Transfer Learning-Based Hyperspectral Image Classification Using Residual Dense Connection Networks.Sensors (Basel, Switzerland) · 2024Article
- Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features.Sensors (Basel, Switzerland) · 2024Article
- Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazelnut oil.Current research in food science · 2024Article
- Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network.Plant methods · 2023Article
- Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies' contributions and deep learning on landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.
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What OpenQuestion holds
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.