Hyperspectral image (HSI) classification has been used to identify material diversity in remote sensing images. Recently, hyperspectral imaging has been applied to historical artifact studies. For example, the Gough Map, one of the earliest surviving maps of Britain, was imaged in 2015 using a hyperspectral imaging system while in the collection at the Bodleian Library, Oxford University. The collection of the HSI data was aimed at pigment analysis for the material diversity of its composition and potentially the timeline of its creation. Traditional methods used spectral unmixing and the spectral angle mapper to classify features in HSIs of historical artifact, those approaches are based only on spectral information of the HSIs. To make full use of both the spatial and spectral features, we developed a novel deep learning technique called 3D-SE-ResNet and applied it to five HSI datasets, including three HSI benchmarks, Indian Pines, Kennedy Space Center, University of Pavia and two HSIs of cultural heritage artifacts, the Gough Map and the Selden Map of China. We trained this deep learning framework to classify pigments in large HSIs with a limited amount of reference (labelled) data automatically. Meanwhile, different spatial and spectral input size and various hyper-parameters of the framework were evaluated. With much less effort and much higher efficiency, this is a breakthrough in object identification and classification in cultural heritage studies that leverages the spectral and spatial information contained in this imagery. Historical geographers, cartographic historians and other scholars will benefit from this work to analyze the pigment mapping of cultural heritage artifacts in the future.
Library of Congress Subject Headings
Hyperspectral imaging; Remote-sensing images--Classification; Machine learning
Imaging Science (Ph.D.)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Bai, Di, "A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods" (2019). Thesis. Rochester Institute of Technology. Accessed from
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