A Perceptually-Informed Approach to Vegetation Identification: Integrating Visual Characteristics and Contextual Information

Mustapha Aliyu and Isa Yunusa Chedi
Additional contact information
Mustapha Aliyu: National Space Research & Development Agency, Obasanjo Space Centre, Musa Yar’Adua Way, Lugbe Abuja.
Isa Yunusa Chedi: National Oil Spill Detection & Response Agency, Abuja, Nigeria

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 7, 1099-1104

Abstract: This study explores the role of visual characteristics in vegetation identification using optical satellite images, with a focus on color information, texture analysis, shape and pattern recognition, and the integration of contextual information. Traditional pixel-based methods relying on isolated spectral signatures have limitations, particularly in complex and heterogeneous landscapes. Recent advances in remote sensing technology have enabled the collection of high-resolution data, providing a wealth of visual information that can be leveraged for more accurate vegetation identification. The study demonstrates the potential of advanced processing techniques, such as Object-based Image Analysis and deep learning models, in extracting and utilizing visual characteristics from high-resolution remote sensing data. These techniques can provide valuable insights into vegetation health, structure, and composition, enabling more informed decision-making. The findings of this study highlight the significance of visual characteristics in vegetation identification and demonstrate the potential of advanced processing techniques in improving land cover classification accuracy. The integration of visual characteristics with contextual information provides a more holistic approach to image analysis, enabling more accurate and robust classifications. This study contributes to the development of more effective and efficient methods for vegetation identification and classification using optical satellite images, and has implications for remote sensing applications in environmental monitoring and management.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrias/ ... ssue-7/1099-1104.pdf (application/pdf)
https://rsisinternational.org/journals/ijrias/arti ... textual-information/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjf:journl:v:10:y:2025:i:7:p:1099-1104

Access Statistics for this article

International Journal of Research and Innovation in Applied Science is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Innovation in Applied Science from International Journal of Research and Innovation in Applied Science (IJRIAS)
Bibliographic data for series maintained by Dr. Renu Malsaria ().