ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-XLIII-B2-2022-801-2022 HIGH PRECISION AUTOMATIC EXTRACTION OF CULTURAL RELIC DISEASES BASED ON IMPROVED SLIC AND AP CLUSTERING Hu C. 1 Huang X. 1 Xia G. 2 Wang Y. 3 Liu X. 4 Ma X. 5 Beijing University of Civil Engineering and Architecture, Beijing, 100044, China China Cultural Heritage Information and Consulting Center, Beijing, 100029, China Beijing Guowenyan Conservation and Development of Cultural Heritage Co., Ltd., Beijing, 100029, China Heilongjiang Surveying and Mapping Measuring Instrument Verification Station, Harbin, 150081, China Beijing Institute of Surveying and Mapping, Beijing, 100038, China 30 05 2022 XLIII-B2-2022 801 807 Copyright: © 2022 C. Hu et al. 2022 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/XLIII-B2-2022/801/2022/isprs-archives-XLIII-B2-2022-801-2022.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLIII-B2-2022/801/2022/isprs-archives-XLIII-B2-2022-801-2022.pdf

Automatic and high-precision detection and quantitative expression of cultural relics diseases are important contents of cultural relics science and technology protection. Aiming at the problem of automatic extraction of boundary cultural relics diseases, this paper proposes an adaptive SLIC0 combined with AP clustering method to achieve high-precision detection of disease areas. Firstly, based on the SLIC0 algorithm, the selected area of the disease orthophoto frame is segmented, and the Canny edge detection is used as the true value. The number of superpixels is iterative until the accuracy meets the requirements, so as to achieve the best fitting of superpixel edges. Then the AP clustering method is used to merge the superpixels of the disease area to obtain the edge information. Finally, taking the surface shedding disease of painted cultural relics as an example, this method is applied to realize the high precision extraction and quantitative expression of the edge of the disease. The correctness, feasibility and advancement of the algorithm are proved by comparing with the existing manual methods. The method in this paper provides an efficient and high-precision means for the quantitative expression of cultural relics diseases, and can provide accurate data support for the scientific and technological restoration of cultural relics.