Few-shot Multispectral Segmentation with Representations Generated by
  Reinforcement Learning
Dilith Jayakody Thanuja Ambegoda
The task of multispectral image segmentation (segmentation of images with
numerous channels/bands, each capturing a specific range of wavelengths of
electromagnetic radiation) has been previously explored in contexts with large
amounts of labeled data. However, these models tend not to generalize well to
datasets of smaller size. In this paper, we propose a novel approach for
improving few-shot segmentation performance on multispectral images using
reinforcement learning to generate representations. These representations are
generated in the form of mathematical expressions between channels and are
tailored to the specific class being segmented. Our methodology involves
training an agent to identify the most informative expressions, updating the
dataset using these expressions, and then using the updated dataset to perform
segmentation. Due to the limited length of the expressions, the model receives
useful representations without any added risk of overfitting. We evaluate the
effectiveness of our approach on several multispectral datasets and demonstrate
its effectiveness in boosting the performance of segmentation algorithms.