Structure-from-motion, multi-view stereo photogrammetry applied to line-scan sediment core images

Abstract

Images of sediment cores are often acquired to preserve primary color information, before such profiles are altered by subsequent sampling and destructive analyses. In many cases, however, no post-processing of these images is undertaken to extract information, despite the fact that image processing can be used to describe and measure structures within the sample. Improvements of RGB (Red/Green/Blue) cameras and image processing algorithms now enable acquisition of highresolution, metrically calibrated pictures called ortho-images, which have great potential. The way to obtain such ortho-images is by processing several raw images. We propose a semi-automated method that uses metrically calibrated targets to create the ortho-image, using Agisoft Photoscan software and a Python script. The method was tested on sediment cores up to 1.5 m long. It was compared to an approach without markers, one that uses only image matching. The proposed method showed better resolution and less distortion (GSD: 59 µm, RMSE: 7-18 µm). Images acquired without calibrated targets can still be used, by manually positioning points that can then be metrically calibrated. This approach is very useful for smartphone images taken in the field. There are many potential applications for such images of sediment cores, for instance as metric stratigraphic logs to facilitate description of the profile by unit, to study and measure structures (e.g. laminae, instantaneous deposits), or use of image registration or data fusion to create spatial landmarks for non-destructive sensors or destructive laboratory analyses. High-resolution metrically calibrated RGB images of sediment cores are simple to acquire and can play an important role in paleoclimate and paleoenvironmental studies.

Reconstructing Paleoflood Occurrence and Magnitude from Lake Sediments

“…HSI analyzes the color of the sample surface using imaging sensors (visible near-infrared, VNIR: 400-1000 nm, and short wave infrared, SWIR: 1000-2500 nm). HSI can be combined with machine learning to characterize mineralogical fingerprints, organic matter and grain-size distribution at a very high sampling resolution, i.e., 60 to 200 µm [109,110] (Figure 5). It is thus a perfect tool to detect detrital input or coarse and graded beds from the continuous sedimentation and it can be used to identify flood layers.…”