Paper Info
Title
A Highly Efficient Method For Training Sample Selection In Remote Sensing Classification
Abstract
Remote sensing classification is an important way to obtain land cover information, and the selection of classification training samples for most of the classification method is an expensive and time-consuming task. However, the traditional training samples selection method is a direct selection based on two-dimensional (2D) images, therefore, training sample selection efficiency is always low in the regions with complex terrain and landscape fragmentation, and the ROI (region of interest) separability is unsatisfactory for classification. This study aims at the low efficiency and low ROI separability for traditional training sample selection method put forward a new training sample selection method using a three-dimensional (3D) terrain model that was created by OLI image fusion digital elevation model (DEM) to select ROIs, which departs from the traditional method based on a two-dimensional image. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the proposed method obtained ROI separability that was greater than 1.9, and with most reaching 2.0; while the ROI separability of traditional method still had unqualified situation, which showed the new method was more effective.
Year
DOI
Venue
2018
10.1109/GEOINFORMATICS.2018.8557085
2018 26TH INTERNATIONAL CONFERENCE ON GEOINFORMATICS (GEOINFORMATICS 2018)
Keywords
Field
DocType
Remote sensing classification, Training samples, 3D terrain, ROI separability, ImageFusion
Data mining,3d terrain,Image fusion,Computer science,Remote sensing,Terrain,Digital elevation model,Region of interest,Sample selection,Land cover,Raised-relief map
Conference
ISSN
Citations 
PageRank 
2161-024X
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
C. Yang129643.66
Qingquan Li21181135.06
Guofeng Wu35420.01
Junyi Chen411.41