Paper Info
Title
Automated Extraction Of Image-Based Endmember Bundles Of Impervious Layer Using Iterative Classification Strategy
Abstract
Endmember variability associated with impervious layer has been a serious problem in spectral mixture analysis (SMA). A reliable spectral library which ideally models the endmember variability is required for precise SMA. Even though many endmember bundles extraction algorithms have been proposed, there are still some problems in these methods which blur the threshold and endmember numbers. In this paper, an iterative classification extraction endmember bundles algorithm (ICEEA) is proposed. Impervious and pervious training sample are provided with GlobeLand30 product, and Maximum Likelihood Classifier (MLC) is used to conduct iteratively classification. After each classification, the artificial layer pixels which are misclassified as pervious class are excluded from artificial cover, and the impervious sample is selected again in new artificial cover. It stops when there are none misclassified pixels existing in the artificial layer. According to the results of simulated 30m data and real TM data, ICEEA has two advantages over PPI: (1) producing more reliable impervious endmember bundles which can model the endmember variability well; (2) having none threshold setting problem; (2) running much faster than PPI.
Year
DOI
Venue
2016
10.1109/IGARSS.2016.7730619
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Keywords
Field
DocType
Image-based endmember bundles, GlobeLand30, Iterative classification
SMA*,Endmember,Impervious surface,Computer vision,Pattern recognition,Computer science,Remote sensing,Image based,Hyperspectral imaging,Artificial intelligence,Maximum likelihood classifier,Pixel
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Fei Xu101.01
Xin Cao2155.20
Xuehong Chen34711.12
Jin Chen425931.87