  Segment Anything Model (SAM) is drastically accelerating the speed and
accuracy of automatically segmenting and labeling large Red-Green-Blue (RGB)
imagery datasets. However, SAM is unable to segment and label images outside of
the visible light spectrum, for example, for multispectral or hyperspectral
imagery. Therefore, this paper outlines a method we call the Multispectral
Automated Transfer Technique (MATT). By transposing SAM segmentation masks from
RGB images we can automatically segment and label multispectral imagery with
high precision and efficiency. For example, the results demonstrate that
segmenting and labeling a 2,400-image dataset utilizing MATT achieves a time
reduction of 87.8% in developing a trained model, reducing roughly 20 hours of
manual labeling, to only 2.4 hours. This efficiency gain is associated with
only a 6.7% decrease in overall mean average precision (mAP) when training
multispectral models via MATT, compared to a manually labeled dataset. We
consider this an acceptable level of precision loss when considering the time
saved during training, especially for rapidly prototyping experimental modeling
methods. This research greatly contributes to the study of multispectral object
detection by providing a novel and open-source method to rapidly segment,
label, and train multispectral object detection models with minimal human
interaction. Future research needs to focus on applying these methods to (i)
space-based multispectral, and (ii) drone-based hyperspectral imagery.
