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.
We applied deep learning techniques to hyperspectral image segmentation and iterative feature sampling. Proposes a greedy scheme to select a subset of highly correlated spectral features in a classification task. The paper explores the use of neural networks for classification and segmentation of hypersepctral imaging (HSI) of cells. Classifying cells and implementing cell segmentation based on deep learning techniques with reduction of input features