Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

Title

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

Creator(s)

Luo, Lei

Wang, Xinyuan

Guo, Huadong

Liu, Chuansheng

Liu, Jie

Li, Li

Du, Xiaocui

Qian, Guoquan

Date

2014

Is Part Of

Remote Sensing

Volume

6

Issue

12

Pages

11956-11976

Language

eng

Rights

http://creativecommons.org/licenses/by/3.0/

ISSN

2072-4292

DOI

https://doi.org/10.3390/rs61211956

URI

https://www.mdpi.com/2072-4292/6/12/11956

Zotero BiDiAr

https://www.zotero.org/groups/5293298/bidiar/items/U355TIHDitem-list

Abstract

Qanats in northern Xinjiang of China provide valuable information for agriculturists and anthropologists who seek fundamental understanding of the distribution of qanat water supply systems with regard to water resource utilization, the development of oasis agriculture, and eventually climate change. Only the tops of qanat shafts (TQSs), indicating the course of the qanats, can be observed from space, and their circular archaeological traces can also be seen in very high resolution imagery in Google Earth. The small size of the TQSs, vast search regions, and degraded features make manually extracting them from remote sensing images difficult and costly. This paper proposes an automated TQS extraction method that adopts mathematical morphological processing methods before an edge detecting module is used in the circular Hough transform approach. The accuracy assessment criteria for the proposed method include: (i) extraction percentage (E) = 95.9%, branch factor (B) = 0 and quality percentage (Q) = 95.9% in Site 1; and (ii) extraction percentage (E) = 83.4%, branch factor (B) = 0.058 and quality percentage (Q) = 79.5% in Site 2. Compared with the standard circular Hough transform, the quality percentages (Q) of our proposed method were improved to 95.9% and 79.5% from 86.3% and 65.8% in test sites 1 and 2, respectively. The results demonstrate that wide-area discovery and mapping can be performed much more effectively based on our proposed method.