8 March 2017 Classification extraction of land coverage in the Ejina Oasis by airborne hyperspectral remote sensing
Author Affiliations +
Proceedings Volume 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016; 102551W (2017) https://doi.org/10.1117/12.2264804
Event: Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 2016, Jinhua, Suzhou, Chengdu, Xi'an, Wuxi, China
Abstract
The hyperspectral data of the Ejina Oasis is performed dimensionality reduction by the minimum noise fraction transform, classified by the maximum likelihood method and clustering processing and finally, the classification of land coverage is obtained. The thesis analyzes the results of two dimensionality reductions, and finds the superiority of the minimum noise fraction. In the classification of land coverage, the overall accuracy and Kappa coefficient are respectively 87.75% and 0.8401. The classification is of high precision and can provide effective parameters for ecological research.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yang Su, Yang Su, Yuan Qi, Yuan Qi, Jianhua Wang, Jianhua Wang, Feinan Xu, Feinan Xu, Jinlong Zhang, Jinlong Zhang, } "Classification extraction of land coverage in the Ejina Oasis by airborne hyperspectral remote sensing", Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 102551W (8 March 2017); doi: 10.1117/12.2264804; https://doi.org/10.1117/12.2264804
PROCEEDINGS
9 PAGES


SHARE
Back to Top