Study on object-oriented urban land use extraction based on Landsat image

Dehao Wang; Ying Han; Xue Qi; Chen Chen

doi:10.1117/12.3029119

16 May 2024 Study on object-oriented urban land use extraction based on Landsat image

Dehao Wang, Ying Han, Xue Qi, Chen Chen

Proceedings Volume 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024); 131660D (2024) https://doi.org/10.1117/12.3029119
Event: International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 2024, Changchun, China

Abstract

Urban land use extraction is an important method to accurately obtain land use information. The object-oriented classification method is mainly used to extract and analyze the land use information of Anshan urban area spanning 10 years from 2009 to 2014 by using spectrum, texture information and vegetation index. The results show that in 2009, 2014 and 2019, the overall classification accuracy was 91%, 87% and 86%, and the kappa coefficient was 85.92%, 79.86% and 77.41%, with good classification accuracy; This paper analyzes the changes in time and space of land use in Anshan City from 2009 to 2019, indicating that the urbanization process of Anshan City has been relatively rapid in the past decade.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dehao Wang, Ying Han, Xue Qi, and Chen Chen "Study on object-oriented urban land use extraction based on Landsat image", Proc. SPIE 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 131660D (16 May 2024); https://doi.org/10.1117/12.3029119

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available