A remote sensing extraction method for built-up area combining nighttime light data and optical remote sensing data in small and medium-sized cities

Minghan Sun; Zhiguo Pang

doi:10.1117/12.3010417

15 November 2023 A remote sensing extraction method for built-up area combining nighttime light data and optical remote sensing data in small and medium-sized cities

Minghan Sun, Zhiguo Pang

Author Affiliations +

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 1281508 (2023) https://doi.org/10.1117/12.3010417
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

As the urbanization takes off, small and medium-sized cities play an increasingly vital role in the rapid expansion of cities. In order to settle the problems encountered in the built-up areas extraction in small and medium-sized cities, such as unclear boundaries of built-up area, confusion between built-up areas and villages or bare area, etc. This paper compares and analyzes different methods for built-up areas extraction in Xinzheng city, in view of NPP/VIIRS data and Sentinel-2B data, and proposes a method to extract built-up area using the fusion data of two types of data after overlaying. The results indicate that the extend of built-up areas may be entirely separated by using night light image, and Sentinel image can completely obtain the spatial distribution information of built-up areas in small and medium-sized cities; Compared with the single-source data extraction method, the extraction method combining two data sources has significantly improved the accuracy of the result. The overall accuracy is 94.60% and Kappa coefficient is 0.84, which is more appropriate for the built-up areas extraction in small and medium-sized cities.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Minghan Sun and Zhiguo Pang "A remote sensing extraction method for built-up area combining nighttime light data and optical remote sensing data in small and medium-sized cities", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 1281508 (15 November 2023); https://doi.org/10.1117/12.3010417

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KEYWORDS

Remote sensing

Data fusion

Vegetation

Data acquisition

Visualization

Image fusion

Data conversion

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