Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation

William M. Severa, Jerilyn A. Timlin, Suraj Kholwadwala, Conrad D. James, James B. Aimone

Abstract: The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements. Motivated by the potential for an in-the-field cell sorting detector, we examine a Synechocystis sp. PCC 6803 dataset wherein cells are grown alternatively in nitrogen rich or deplete cultures. We use deep learning techniques to both successfully classify cells and generate a mask segmenting the cells/condition from the background. Further, we use the classification accuracy to guide a data-driven, iterative feature selection method, allowing the design neural networks requiring 90% fewer input features with little accuracy degradation.

TL;DR: We applied deep learning techniques to hyperspectral image segmentation and iterative feature sampling.

Keywords: Applied deep learning, Image segmentation, Hyperspectral Imaging, Feature sampling