III: Medium: Collaborative Research: Deep Learning in Spectroscopic Domains

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Many problems in science today require the analysis of massive datasets. This project investigates the fundamental problem of extracting latent hidden regularities from high-dimensional scientific data sets, specifically from two different types of spectroscopic measurements -- three-dimensional hyperspectral imaging used in remote sensing of the Earth and other planets, and one-dimensional spectral signals arising from chemical analyses from laser-induced breakdown spectroscopy (LIBS), such as used currently by the Curiosity rover on Mars. The project is applying recent advances in deep learning, optimization, and machine learning to practical real-world scientific applications involving the analysis of materials from Earth and outer space, such as Mars, as well as the mapping of Martian and terrestrial surfaces through hyperspectral imagery. Deep learning uses multi-layer neural networks to construct a hierarchy of latent representations of high-dimensional datasets. This project designs novel architectures and algorithms for deep learning, and applies them to spectroscopic domains, such as LIBS and hyperspectral imaging. Three challenges from spectroscopic domains guide the research. First, in many applications such as the Curiosity rover on Mars, the number of available LIBS spectra are limited as it requires an active sensing operation followed by transmission of data by a robot situated millions of miles from Earth. A further challenge is that data from Mars is inherently unlabeled, and instrumental variations and terrain variations between Earth and Mars require solving a key transfer learning problem. For hyperspectral imaging, the project is extending work on deep learning applied to two-dimensional images to data that involves two spatial dimensions as well as the third spectral dimension, where images are recorded at multiple wavelengths. This project explores a variety of ways of designing new convolutional neural networks and other approaches that can effectively exploit the third spectral dimension.