Abstract: Hyperspectral imaging is one of the most widespread remote sensing techniques in earth observation, corresponding to images with high spectral and spatial resolution that enable material detection through the identification of their spectral signature. A key challenge in hyperspectral imaging is the definition of novel and efficient computational methods that contribute to reducing computational cost while maintaining the efficacy and precision in material detection provided by methods such as correlation or machine learning. This study aims to propose a new efficient method for vegetation detection in hyperspectral images based on the similarity between the approximate and detailed components of the Haar wavelet transform of the vegetation spectral signature, with respect to the components of the pixel to be classified in the image. For the development of the present investigation, five methodological phases were defined: P1. Selection of sample pixels for vegetation and other materials; P2. Determination of the characteristic vegetation pixel; P3. Implementation and evaluation of the method with vegetation and non-vegetation pixels; P4. Deployment of the method on the reference hyperspectral image; P5. Comparative evaluation of the proposed method against the correlation method. As a result of this research, a novel computational method for vegetation identification in hyperspectral images was proposed, leveraging the similarity of wavelet transform components. This method demonstrated comparable detection efficacy to the correlation method and proved to be approximately 5% more efficient in the detection process. The proposed method can be suitably integrated into hyperspectral image-based environmental monitoring systems, particularly where images are of considerable size and more efficient methods are required.

Gabriel Elías Chanchí Golondrino, Manuel Alejandro Ospina Alarcón and Manuel Saba. “Vegetation Identification in Hyperspectral Images of Cartagena City Using the Haar Wavelet Transform”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161230

@article{Golondrino2025,
title = {Vegetation Identification in Hyperspectral Images of Cartagena City Using the Haar Wavelet Transform},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161230},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161230},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Gabriel Elías Chanchí Golondrino and Manuel Alejandro Ospina Alarcón and Manuel Saba}
}