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

当今科学中的许多问题都需要对大量数据集进行分析。该项目研究了从高维科学数据集中提取潜在隐藏规律的基本问题，特别是从两种不同类型的光谱测量中提取——用于地球和其他行星遥感的三维高光谱成像，以及激光诱导击光光谱（LIBS）化学分析产生的一维光谱信号，如好奇号火星探测器目前使用的光谱。该项目将深度学习、优化和机器学习方面的最新进展应用于实际的现实世界科学应用，包括对来自地球和外太空（如火星）的材料进行分析，以及通过高光谱图像绘制火星和地球表面。深度学习使用多层神经网络来构建高维数据集的潜在表示层次结构。该项目为深度学习设计了新颖的架构和算法，并将其应用于光谱领域，如LIBS和高光谱成像。来自光谱领域的三个挑战指导了研究。首先，在许多应用中，如火星上的好奇号探测器，可用的LIBS光谱数量有限，因为它需要主动传感操作，然后由距离地球数百万英里的机器人传输数据。进一步的挑战是，来自火星的数据本质上是未标记的，地球和火星之间的仪器变化和地形变化需要解决一个关键的迁移学习问题。对于高光谱成像，该项目正在将应用于二维图像的深度学习工作扩展到涉及两个空间维度以及第三个光谱维度的数据，其中图像以多个波长记录。该项目探索了设计新的卷积神经网络和其他可以有效利用第三光谱维度的方法的各种方法。

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.