Multispectral image visualization and NDVI-based anomaly detection

Abstract

Satellite imagery is essential for advancing precision agriculture, providing the foundation for accurate monitoring and decision-making. A key initial step in analyzing multispectral images is achieving a visualization that accurately reflects real-world conditions. Effective visualization mechanisms, such as those driven by Machine Learning (ML), depend on the availability of well-structured datasets. This study investigates how characteristics such as the quantity, variability, and quality of datasets influence the quality of visualization outcomes. Our findings highlight that data quantity and variability are more critical than quality in achieving optimal results. The research also explores the use of standard Macbeth color charts to improve dataset consistency in spectral analysis. Additionally, we propose a straightforward and computationally efficient methodology for interpreting and analyzing multispectral images in the context of anomaly detection. Using the Normalized Difference Vegetation Index (NDVI), this approach enables automatic identification of potential crop development issues, offering practical benefits for agricultural health monitoring and early intervention strategies. By addressing the challenges of data availability and proposing an anomaly detection methodology, this study provides valuable insights into the use of satellite imagery for monitoring and improving agricultural practices. These findings contribute to the broader understanding of optimizing satellite imagery analysis for agricultural applications.