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arXiv · 1710.09934

Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation

William M. Severa·Jerilyn A. Timlin·Suraj Kholwadwala·Conrad D. James·James B. Aimone
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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 $\textit{Synechocystis sp.}$ 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.

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William M. Severa, Jerilyn A. Timlin, Suraj Kholwadwala, Conrad D. James, James B. Aimone. 2017-10-26. Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation. https://arxiv.org/abs/1710.09934

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