Academic Journal
Hyperspectral image processing techniques for environmental monitoring: a comprehensive review.
| Title: | Hyperspectral image processing techniques for environmental monitoring: a comprehensive review. |
|---|---|
| Authors: | Lekha PS; Siddhartha Academy of Higher Education Deemed to Be University, Vijayawada, Andhra Pradesh, 520007, India. 24edp13012@vrsec.ac.in., Sirisha BL; Siddhartha Academy of Higher Education Deemed to Be University, Vijayawada, Andhra Pradesh, 520007, India. |
| Source: | Environmental monitoring and assessment [Environ Monit Assess] 2026 Jun 27; Vol. 198 (7). Date of Electronic Publication: 2026 Jun 27. |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1998- : Dordrecht : Springer Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981- |
| MeSH Terms: | Environmental Monitoring*/methods , Hyperspectral Imaging*/methods , Remote Sensing Technology* , Image Processing, Computer-Assisted* |
| Abstract: | Remote sensing and environmental monitoring have significantly advanced with the emergence of hyperspectral image processing techniques, offering unparalleled detail in spectral analysis. Traditional remote sensing methods, such as multispectral and panchromatic imaging, often lack the spectral resolution necessary to detect subtle environmental changes. This limitation hampers the accuracy of monitoring applications such as vegetation stress, pollution detection, and land cover classification. This study reviews hyperspectral image processing techniques to enhance the accuracy of environmental monitoring. It aims to improve the detection and classification of subtle changes in land cover, vegetation health, and pollution levels. The study explores the evolving landscape of hyperspectral image processing methods and their critical role in remote sensing applications. Techniques for spectral and spatial feature extraction, dimensionality reduction, and data fusion address the complexity of hyperspectral data. Challenges like high dimensionality, noise, limited labeled data, and model interpretability are discussed. The review also highlights recent advancements, including deep learning architectures, attention mechanisms, transfer learning, generative models, and cloud-based solutions for real-time processing. Practical applications in land cover classification, vegetation health, water quality assessment, disaster response, and urban development are examined. These integrated approaches aim to enhance monitoring accuracy, efficiency, and decision-making in real-world scenarios. Future research may focus on improving real-time processing capabilities through edge computing and AI-driven models, expanding labeled datasets, and enhancing model interpretability to further advance hyperspectral imaging applications. (© 2026. The Author(s), under exclusive licence to Springer Nature Switzerland AG.) |
| Competing Interests: | Declarations. Ethics approval: The paper has been, submitted with full responsibility, following due ethical procedure, and there is no duplicate publication, fraud, or plagiarism. None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper. This article does not contain any studies with human participants or animals performed by any of the authors. Consent to participate: Not applicable. Consent for publication: Not applicable. Conflict of interest: The authors declare no conflict of interest. Cover letter: This manuscript is the authors’ original work and has not been published nor has it been, submitted simultaneously elsewhere. All authors have checked the manuscript and have agreed to the submission. |
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| Contributed Indexing: | Keywords: Disaster response; Hyperspectral image; Land cover classification; Urban development; Vegetation health; Water quality assessment |
| Entry Date(s): | Date Created: 20260627 Date Completed: 20260628 Latest Revision: 20260707 |
| Update Code: | 20260707 |
| DOI: | 10.1007/s10661-026-15629-y |
| PMID: | 42364045 |
| Database: | MEDLINE |
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