A STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data

The changes of urban built-up areas can reflect the process of
urbanization, and it can reflect the population, economy, and cultural
development of the city. Therefore, accurate and timely extraction of
urban built-up areas plays an important role in the dynamic management of
the city. In the existing research, single-source remote sensing data is
used to extract urban built-up areas, and there is a problem that the
spectrum of urban areas and non-urban areas is easily confused.
Multi-source remote sensing data, including luojia-1 remote sensing data,
Landsat 8 OLI remote sensing data, etc., can make up for the spectrum
confusing issues. We fuse the time series information of night light
remote sensing data, neighborhood information and point of interest (POI)
data in spatial dimension, and propose a built-up area extraction method
that integrates night light time and space information and POI
information.