🧑 Andreea Nițu, Corneliu Florea, Mihai Ivanovici, Andrei Racoviteanu
📅 June 2025
Vegetation indices have long been central to vegetation monitoring through remote sensing. The most popular one is the Normalized Difference Vegetation Index (NDVI), yet many vegetation indices (VIs) exist. In this paper, we investigate their distinctiveness and discriminative power in the context of applications for agriculture based on hyperspectral data. More precisely, this paper merges two complementary perspectives: an unsupervised analysis with PRISMA satellite imagery to explore whether these indices are truly distinct in practice and a supervised classification over UAV hyperspectral data. We assess their discriminative power, statistical correlations, and perceptual similarities. Our findings suggest that while many VIs have a certain correlation with the NDVI, meaningful differences emerge depending on landscape and application context, thus supporting their effectiveness as discriminative features usable in remote crop segmentation and recognition applications.
https://doi.org/10.3390/s25123817
Nițu, A., Florea, C., Ivanovici, M., & Racoviteanu, A. (2025). NDVI and Beyond: Vegetation Indices as Features for Crop Recognition and Segmentation in Hyperspectral Data. Sensors, 25(12), 3817. https://doi.org/10.3390/s25123817
@Article{s25123817,
AUTHOR = {Nițu, Andreea and Florea, Corneliu and Ivanovici, Mihai and Racoviteanu, Andrei},
TITLE = {NDVI and Beyond: Vegetation Indices as Features for Crop Recognition and Segmentation in Hyperspectral Data},
JOURNAL = {Sensors},
VOLUME = {25},
YEAR = {2025},
NUMBER = {12},
ARTICLE-NUMBER = {3817},
URL = {https://www.mdpi.com/1424-8220/25/12/3817},
PubMedID = {40573704},
ISSN = {1424-8220},
ABSTRACT = {Vegetation indices have long been central to vegetation monitoring through remote sensing. The most popular one is the Normalized Difference Vegetation Index (NDVI), yet many vegetation indices (VIs) exist. In this paper, we investigate their distinctiveness and discriminative power in the context of applications for agriculture based on hyperspectral data. More precisely, this paper merges two complementary perspectives: an unsupervised analysis with PRISMA satellite imagery to explore whether these indices are truly distinct in practice and a supervised classification over UAV hyperspectral data. We assess their discriminative power, statistical correlations, and perceptual similarities. Our findings suggest that while many VIs have a certain correlation with the NDVI, meaningful differences emerge depending on landscape and application context, thus supporting their effectiveness as discriminative features usable in remote crop segmentation and recognition applications.},
DOI = {10.3390/s25123817}
}