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

III：媒介：协作研究：光谱领域的深度学习

• 批准号: 1564083
• 负责人: Melinda Dyar
• 金额: $ 39.42万
• 依托单位: Mount Holyoke College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564083&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Deep

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.

当今科学中的许多问题都需要对大量数据集进行分析。该项目研究从高维科学数据集，特别是从两种不同类型的光谱测量-地球和其他行星遥感中使用的三维超光谱成像和激光诱导击穿光谱化学分析产生的一维光谱信号-中提取潜在隐藏光谱的基本问题，例如目前在火星上使用的Currency漫游者。该项目正在将深度学习、优化和机器学习的最新进展应用于实际的现实世界科学应用，包括分析来自地球和外太空（如火星）的材料，以及通过高光谱图像绘制火星和陆地表面。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的层次结构。该项目为深度学习设计了新的架构和算法，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这项研究。首先，在许多应用中，例如火星上的Currency漫游者，可用的LIBS光谱的数量是有限的，因为它需要主动感测操作，然后由距离地球数百万英里的机器人传输数据。另一个挑战是，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卷积神经网络的各种方法，以及其他可以有效利用第三光谱维度的方法。