Deep Learning for Hyperspectral Image Segmentation in Biosecurity Scanning

Biosecurity scanning plays a crucial role in preventing exotic pests, weeds and contaminants from entering a country through shipping containers. Exposure to biosecurity risks causes a substantial loss to the native environment, production value and public health. Currently, these threats are managed via manual inspection, detector dogs and x-ray scanners; however, these procedures are time-consuming, error-prone, or costly. In this research, we propose a novel approach for biosecurity risk detection that utilizes hyperspectral imaging technology and semantic image segmentation. This approach segments the target objects in a hyperspectral image by analyzing their spatial and spectral signatures. The target objects in this project include metal, plants, soil, creatures and background.