Accurately assessing the shape, size, and modality of features in rock samples is a longstanding problem in geology. Recent advances in machine learning have introduced the possibility of performing these tasks through automated image analysis. To leverage these methods for geological and paleontological applications, we first need a way to acquire high-resolution images of polished slabs and thin sections with a field of view large enough to fit samples containing crystals, fossils, bedforms, etc. We describe a new multispectral setup that can acquire images at ∼3.76 mm per pixel spatial resolution over a 21 cm2 field of view, equipped with 8-band (470-940 nm) spectral resolution, plus a band for ultraviolet (365 nm) fluorescence. Additionally, we present a 5-band (470-940 nm) light table with automated rotating polarizers, which allows use of the camera as a high-throughput transmitted light thin section imager. The use of color bands outside the visible spectrum, as well as the registration of multiple cross-polarized rotations, encode rock properties that heighten image contrast and improve the accuracy of machine learning models. Our setup and methods provide an efficient way to (1) build reproducible image archives of rock specimens to complement field observations, (2) classify and segment those images, and (3) quantitatively compare lithofacies and fossil assemblages.
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