Construction Land Information Extraction and Expansion Analysis of Xiaogan City Using One-Class Support Vector Machine

Abstract

Urban expansion is generally accompanied by a series of ecological problems; therefore, it is of great significance to strengthen the research on urban expansion to effectively guide and control urban expansion. In this study, we used a one-class support vector machine (OCSVM) based on Landsat image data to extract the construction land area in Xiaogan City of Hubei Province (China) in 2000, 2005, 2010, 2015, and 2020. We analyzed the characteristics of construction land expansion and explore the driving mechanisms of construction land expansion in Xiaogan City. The results show that 1) the accuracy of the construction land information extracted by OCSVM was 91.46%, 90.02%, 89.31%, 92.23%, and 89.67%, respectively, which met the expected results and could be used for the study of extension and driving mechanism, proving that OCSVM is suitable for the study of remote sensing image classification when only one class of features is extracted; 2) Xiaogan City's overall expansion is of high-speed type in 20 years, which is restricted by the terrain, medium-speed expansion between 2000 and 2005, and high-speed expansion between 2005 and 2020, and the expansion intensity of Xiaogan City in all four time periods from 2000 to 2020 is of slow expansion type; and 3) among the main factors influencing urban expansion in Xiaogan, the increase in population, economic development, development of high-tech zones, and construction of transportation lines increase the demand for construction land; government planning decisions provide the direction and scope for the expansion of construction land, which is the most leading factor among the drivers of construction land expansion.

Keywords

• Construction land
• one-class support vector machine (OCSVM)
• remote sensing image classification
• urban expansion
• Xiaogan city