Abstract
The Han Dynasty Great Wall (GH), one of the largest and most significant ancient defense projects in the whole of northern China, has been studied increasingly not only because it provides important information about the diplomatic and military strategies of the Han Empire (206 B.C.–220 A.D.), but also because it is considered to be a cultural and national symbol of modern China as well as a valuable archaeological monument. Thus, it is crucial to obtain the spatial pattern and preservation situation of the GH for next-step archaeological analysis and conservation management. Nowadays, remote sensing specialists and archaeologists have given priority to manual visualization and a (semi-) automatic extraction approach is lacking. Based on the very high-resolution (VHR) satellite remote sensing imagery, this paper aims to identify automatically the archaeological features of the GH located in ancient Dunhuang, northwest China. Gaofen-1 (GF-1) data were first processed and enhanced after image correction and mathematical morphology, and the M-statistic was then used to analyze the spectral characteristics of GF-1 multispectral (MS) data. In addition, based on GF-1 panchromatic (PAN) data, an auto-identification method that integrates an improved Otsu segmentation algorithm with a Linear Hough Transform (LHT) is proposed. Finally, by making a comparison with visual extraction results, the proposed method was assessed qualitatively and semi-quantitatively to have an accuracy of 80% for the homogenous background in Dunhuang. These automatic identification results could be used to map and evaluate the preservation state of the GH in Dunhuang. Also, the proposed automatic approach was applied to identify similar linear traces of other generations of the Great Wall of China (Western Xia Dynasty (581 A.D.–618 A.D.) and Ming Dynasty (1368 A.D.–1644 A.D.)) in various geographic regions. Moreover, the results indicate that the computer-based automatic identification has great potential in archaeological research, and the proposed method can be generalized and applied to monitor and evaluate the state of preservation of the Great Wall of China in the future.
Highlights
Introduction(GWC)that thatwe wesee seetoday today was built during largeproportion proportion of of the the Great
Values calculated from the spectral channels and their derived products were less than 1.0. This is because linear traces do not have distinctive spectral signatures, being partially or completely covered by layers of water-eroded or wind-weathered soils, gravels or sands (Figure 5)
Based theapproximately results of field surveys, it was knownthat thathad a features in the image, we focused ononthe rectangular patches the length of the linear trace is often in the range m to m—equivalent to pixels in the small length to width ratio
Summary
(GWC)that thatwe wesee seetoday today was built during largeproportion proportion of of the the Great. Great Wall was built during thethe Ming Dynasty (1368 A.D.–1644 A.D.). At the same time, based on the automatically identified results, the detected lines were converted into shapefiles, and the length of the automatically extracted linear traces is calculated in ArcGIS 10.1. These were compared with the manually extracted length to assist in the quantitative evaluation of the accuracy and reliability of the proposed approach.
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