A new method of extracting built-up area based on multi-source remote sensing data: a case study of Baoding central city, China

Abstract

The accurate extraction of Built-Up Areas (BUAs) is of great significance for analysing urban spatial evolution patterns. In this study, we proposed a new method for high-precision extraction of BUA based on multi-source remote sensing data. Firstly, Built-Up Area Extraction Index (BAEI) was used to preliminarily identify the BUA based on Landsat 8 imagery. Secondly, the Support Vector Machine (SVM) algorithm was used for improving the extraction precision of BUA, whose selected training sample was established on the Nighttime Light (NTL) data. Then, images fusion and continuity correction were carried out. Finally, the Neighbourhood Statistics Analysis (NSA) was used to adjust and remove the part of the non-urban centre which was misjudged as the BUA. Our results show that this method has better performance on both overall accuracy and Kappa coefficient compared with other classic methods, which provides empirical reference for understanding law of land expansion and rational land planning.

Keywords:

• Built-up area
• support vector machine
• built-up area extraction index
• neighbourhood statistics analysis
• nighttime light data

Data availability statement

The data that support the findings of this study are available from the corresponding author, Zenglei Xi, upon reasonable request.

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Funding

This work was supported by the National Social Science Fund of China under Grant number 17BTJ029.