Hyperspectral Features for Contaminated Soil Detection

Different anthropogenic activities, both legal and illegal, can potentially induce soil contamination, which represents a risk to both the environment and human health. Utilizing Remote Sensing technologies to identify the effects of soil contamination on the local plant enables rapid recognition of these environmental hazards, facilitating more effective management and control. In this context, the use of hyperspectral data acquired from UAVs can be very useful. Hyperspectral imaging captures a much larger number of narrow spectral bands than multispectral imaging, allowing for more precise and detailed spectral information to be extracted from the captured data. In this paper, we leverage a set of hyperspectral indices to train a Support Vector Machine (SVM) for discriminating between plants grown in soil contaminated with substances such as Pb, Cr, Zn, and Benzopyrene, and those grown in uncontaminated soil. This approach enables us to harness the rich spectral information provided by hyperspectral data to better identify vegetation changes due to contamination, enabling us to detect environmental issues with higher reliability compared to using multispectral data.