Deep Learning Spatial-Spectral Processing of Hyperspectral Images for Pigment Mapping of Cultural Heritage Artifacts

Abstract

In 2015, the Gough Map was imaged using a hyperspectral imaging system while in the collection at the Bodleian Library, University of Oxford. It is one of the earliest surviving maps of Britain. Hyperspectral image (HSI) classification has been widely used to identify materials in remotely sensed images. Recently, hyperspectral imaging has been applied to historical artifact studies. The collection of the HSI data of the Gough Map was aimed at pigment mapping for towns and writing with different spatial patterns and spectral (color) features. We developed a spatial-spectral deep learning framework called 3D-SE-ResNet to automatically classify pigments in large HSI of cultural heritage artifacts with limited reference (labelled) data and have applied it to the Gough Map. 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, providing codicological information to cartographic historians.