Comparison of five common land cover supervised classification algorithms based on GF-2 and Landsat8 data

Jiakun Li; Jianhua Huang; Xiaomao Chen; Yang Bai; Huien Shi; Yu Xiao

doi:10.1117/12.2589321

20 January 2021 Comparison of five common land cover supervised classification algorithms based on GF-2 and Landsat8 data

Jiakun Li, Jianhua Huang, Xiaomao Chen, Yang Bai, Huien Shi, Yu Xiao

Author Affiliations +

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 1171906 (2021) https://doi.org/10.1117/12.2589321
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

With the development of remote sensing technology and the differences in remote sensing image classification, it is particularly important to be able to accurately use classification methods to classify images and to compare classification algorithms. In this paper, taking Yangshuo County as the research area, five common supervised classifications, namely support vector machine (SVM), maximum likelihood classification (MLC), neural network (NN), spectral angle mapping (SAM) and spectral information divergence (SID), are used to classify the land cover of remote sensing image data of GF- 2、Landsat8 and its fusion in the same area. The classification results are obtained and compared. Moreover, the overall classification accuracy (OA) and Kappa coefficient are used to evaluate the performance of the image classification algorithm. The results show that both MLC and SVM perform best on these three data sets. For higher spatial resolution GF-2 and fusion data, the OA and Kappa coefficients of both image data classifiers is 10% higher than those of Landsat8 data with higher spectral resolution.

Citation Download Citation

Jiakun Li, Jianhua Huang, Xiaomao Chen, Yang Bai, Huien Shi, and Yu Xiao "Comparison of five common land cover supervised classification algorithms based on GF-2 and Landsat8 data", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 1171906 (20 January 2021); https://doi.org/10.1117/12.2589321

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