Documentation and Detection of Colour Changes of Bas Relieves Using Close Range Photogrammetry

Abstract

ABSTRACT:The digitization of complex buildings, findings or bas relieves can strongly facilitate the work of archaeologists, mainly for in depth analysis tasks. Notwithstanding, whether new visualization techniques ease the study phase, a classical naked-eye approach for determining changes or surface alteration could bring towards several drawbacks. The research work described in these pages is aimed at providing experts with a workflow for the evaluation of alterations (e.g. color decay or surface alterations), allowing a more rapid and objective monitoring of monuments. More in deep, a pipeline of work has been tested in order to evaluate the color variation between surfaces acquired at differentépoques. The introduction of reliable tools of change detection in the archaeological domain is needful; in fact, the most widespread practice, among archaeologists and practitioners, is to perform a traditional monitoring of surfaces that is made of three main steps: production of a hand-made map based on a subjective analysis, selection of a sub-set of regions of interest, removal of small portion of surface for in depth analysis conducted in laboratory. To overcome this risky and time consuming process, digital automatic change detection procedure represents a turning point. To do so, automatic classification has been carried out according to two approaches: a pixel-based and an object-based method. Pixel-based classification aims to identify the classes by means of the spectral information provided by each pixel belonging to the original bands. The object-based approach operates on sets of pixels (objects/regions) grouped together by means of an image segmentation technique. The methodology was tested by studying the bas-relieves of a temple located in Peru, named Huaca de la Luna. Despite the data sources were collected with unplanned surveys, the workflow proved to be a valuable solution useful to understand which are the main changes over time.

How to Extract Useful Information About the Decay of Bass Relieves in Archaeological Area

“…The paper presents the following structure, in the Sec 2. report a description of the state of the art in the change detection application from CH point of view and from the computer vision point of view; Sec 3. explain the application context; Sec 4. describes the work flow applied and the process useful to the change detection from a computer vision point of view. At the end the Sec 5. show the results obtained and conclude with the effectiveness of the previous work (Malinverni et al, 2017) respect to a completely automatic work flow.…”

“…From the computation, we obtained Fig. 9 that shows the results of the supervised clustering from (Malinverni et al, 2017) on the 2017 data set with respect to the 2006. As visible, the results show a homogeneity on changes that is more realistic.…”

“…This stage of the work follows our previous research (Malinverni et al, 2017), where a supervised approach was applied. The hard thematic map is used for comparison with the un supervised change map.…”

“…Their resolution can be exploited to perform image analysis that might support archaeologist in their work of understanding material decay; in fact, sometimes a naked eye approach may result insufficient and the support of image classification techniques may result fundamental. In this work, the methodology proposed in (Malinverni et al, 2017) for the evaluation of color degradation between the decorative representations acquired at different époques is extended. A new dataset has been collected in 2017, on the same bas-relieves located at Huaca de la Luna in Peru, to make a comparison with the ones collected in 2006 and 2012.…”

Automatic Point Cloud Segmentation for the Detection of Alterations on Historical Buildings Through an Unsupervised and Clustering-Based Machine Learning Approach

“…On the other hand, colour-based segmentation has been performed mainly on 2D images (Malinverni et al, 2017;Vorobel et al, 2021), or in some cases on 3D data (Valero et al, 2019;Galantucci et al, 2020), for the detection of decay patterns recognizable for their predominant colours or their chromatic differences. Usually standard colour information of images or three-dimensional data is expressed in RGB, which is not a suitable space for a colour-based segmentation, because the spatial proximity, corresponding to the geometric distance between colour-values, is not coherent with the perceptive similarity among colours (Sonka et al, 2014;Gonzalez and Woods, 2018).…”