Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized approach...

Full description

Bibliographic Details
Main Authors: Xiaolong Ma, Xiaohua Tong, Sicong Liu, Xin Luo, Huan Xie, Chengming Li
Format: Article
Language:English
Published: MDPI AG 2017-03-01
Series:Remote Sensing
Subjects:
urban built-up areas
sample-optimized approach
SVM classification
DMSP-OLS
initial training samples
GlobeLand30
NDVI
probability threshold
iterative optimization process
Online Access:http://www.mdpi.com/2072-4292/9/3/236

Similar Items

Similar Items